1 Simple linear model using brms

If the interaction term is included in the model, the brms package automatically inserts an intercept and the main effect of the treatment (A) into the fitted Bayesian model.

Thus, the simple linear model with the brms package would be:

\[ \begin{aligned} logit(P(Y_i=1)) &= \tau + \boldsymbol{X_i}^\top \boldsymbol{m} +(A_i-\bar{A})\beta_0 + (A_i-\bar{A})\boldsymbol{X_i}^\top\boldsymbol{\beta}\\ \tau &\sim Normal(df=3,\mu=0,\sigma=8)\\ \boldsymbol{m} &\sim Normal(\boldsymbol\mu=0,\Sigma=\ 5^2 \boldsymbol{I}_{p\times p})\\ \beta_0 &\sim Normal(\mu=0,\sigma=5)\\ \boldsymbol{\beta} &\sim Normal(\mu=\boldsymbol{0},\Sigma=5^2 \boldsymbol{I}_{p\times p})\\ \end{aligned} \]

2 Simulation results for all scenarios

library("collapse")
library("dplyr")
library("data.table")
library("tidyverse")
library("kableExtra")
library("table1")
#load("C:/Users/Danni/OneDrive - NYU Langone Health/Side projects/BayesSIMML/results_testlinearcode/Submit_v2/results/all_24_scenarios_brms.rda")
load("all_24_scenarios_brms.rda")
results <- unlist2d(results.aggregated2, idcols = "replicate",DT = TRUE)#the first columns: scenario's id;the second column:simulation's id for each secnario
pri_scen <- function(x){
  paste0(x["replicate"],". ","n=",x["n"],","," p=",x["p"],","," g.choice=",x["g.choice"],",",
         " m.choice=",x["m.choice"])
}

results$scenario <- apply(results,1,pri_scen)
kable(results, "html") %>% kable_styling("striped") %>% scroll_box(height = "300px")
replicate iter n p g.choice m.choice accept.rate bsim.value opt.value diff.value value.all.get.1 value.all.get.2 bsim.deviance bsim.accuracy sng.bayes.accuracy sng.bayes.value sng.bayes.deviance sng.bayes.diff.value div GR.beta.psrf.1.03 GR.beta.psrf.1.05 GR.beta.psrf.1.07 GR.gamma.psrf.1.03 GR.gamma.psrf.1.05 GR.gamma.psrf.1.07 GR.m.psrf.1.03 GR.m.psrf.1.05 GR.m.psrf.1.07 GR.sgbayes.psrf.1.03 GR.sgbayes.psrf.1.05 GR.sgbayes.psrf.1.07 scenario
1 1 500 5 linear linear 0.6515 0.4535613 0.4390499 0.0145114 0.5027023 0.5032988 1.336834 0.7934 0.8421 0.4465543 1.326334 0.0075044 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 2 500 5 linear linear 0.6920 0.4574747 0.4351532 0.0223215 0.4994010 0.4980779 1.335115 0.7227 0.8396 0.4435441 1.328364 0.0083909 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 3 500 5 linear linear 0.6830 0.4752059 0.4368110 0.0383949 0.4997128 0.5016140 1.335135 0.6582 0.8718 0.4420441 1.333354 0.0052331 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 4 500 5 linear linear 0.7055 0.4486613 0.4358281 0.0128332 0.4986235 0.5013672 1.351155 0.8237 0.8133 0.4468610 1.349178 0.0110329 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 5 500 5 linear linear 0.7030 0.4713381 0.4360923 0.0352458 0.4991748 0.4995987 1.366748 0.6559 0.6714 0.4677606 1.356346 0.0316683 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 6 500 5 linear linear 0.7135 0.4530584 0.4348557 0.0182027 0.4997515 0.4971493 1.338692 0.7576 0.7738 0.4502364 1.336855 0.0153807 27 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 7 500 5 linear linear 0.6490 0.4760533 0.4363097 0.0397436 0.4991309 0.4995586 1.332310 0.6032 0.7856 0.4495765 1.326662 0.0132668 20 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 8 500 5 linear linear 0.6650 0.4599948 0.4359839 0.0240109 0.4988706 0.5004383 1.361241 0.7713 0.8055 0.4473188 1.362568 0.0113349 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 9 500 5 linear linear 0.6705 0.4490487 0.4365067 0.0125420 0.5007987 0.4991509 1.364823 0.8010 0.8750 0.4415596 1.343759 0.0050529 18 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 10 500 5 linear linear 0.7415 0.4468761 0.4349147 0.0119614 0.4991870 0.4984760 1.324591 0.8070 0.7559 0.4522938 1.320716 0.0173791 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 11 500 5 linear linear 0.7255 0.4832797 0.4353566 0.0479231 0.4990050 0.4997683 1.338164 0.5828 0.8226 0.4452327 1.321626 0.0098761 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 12 500 5 linear linear 0.7280 0.4497354 0.4353859 0.0143495 0.4999477 0.4984206 1.340308 0.7787 0.8012 0.4478179 1.340558 0.0124320 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 13 500 5 linear linear 0.6510 0.4581135 0.4336301 0.0244834 0.4979256 0.4981605 1.333905 0.7989 0.7781 0.4481875 1.330085 0.0145573 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 14 500 5 linear linear 0.7300 0.4410426 0.4365496 0.0044930 0.4986927 0.5000575 1.341441 0.8871 0.8989 0.4398508 1.340296 0.0033013 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 15 500 5 linear linear 0.6495 0.4621938 0.4360478 0.0261460 0.5002078 0.5007307 1.341634 0.7031 0.8990 0.4395801 1.321324 0.0035322 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 16 500 5 linear linear 0.6260 0.4666076 0.4370934 0.0295142 0.5019731 0.4985837 1.356609 0.7304 0.7791 0.4516702 1.359095 0.0145768 24 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 17 500 5 linear linear 0.5105 0.4890999 0.4372031 0.0518968 0.4993158 0.5025821 1.378376 0.5615 0.8143 0.4478242 1.350425 0.0106211 19 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 18 500 5 linear linear 0.6445 0.4709799 0.4359302 0.0350497 0.4984294 0.5012868 1.350918 0.6377 0.7709 0.4523075 1.346073 0.0163773 24 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 19 500 5 linear linear 0.6510 0.4509532 0.4335209 0.0174323 0.4987479 0.4976985 1.348610 0.7647 0.7833 0.4488300 1.344498 0.0153091 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 20 500 5 linear linear 0.6755 0.4387491 0.4360287 0.0027204 0.4986326 0.5002290 1.316634 0.9068 0.8861 0.4398532 1.311474 0.0038245 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 21 500 5 linear linear 0.4980 0.4890105 0.4374889 0.0515216 0.5010177 0.5010085 1.364763 0.5562 0.6847 0.4661090 1.343518 0.0286201 22 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 22 500 5 linear linear 0.7345 0.4911732 0.4387318 0.0524414 0.5005199 0.5031905 1.361506 0.5743 0.8426 0.4466663 1.338416 0.0079345 22 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 23 500 5 linear linear 0.6860 0.4874700 0.4382021 0.0492679 0.5009506 0.5020067 1.336300 0.5563 0.9049 0.4410480 1.334407 0.0028459 24 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 24 500 5 linear linear 0.7365 0.4437951 0.4363402 0.0074549 0.4991211 0.5014444 1.327097 0.8839 0.8632 0.4422883 1.320123 0.0059481 19 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 25 500 5 linear linear 0.6720 0.4814761 0.4368940 0.0445821 0.5006343 0.5026005 1.334147 0.6364 0.8101 0.4484179 1.326637 0.0115239 18 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 26 500 5 linear linear 0.6345 0.4812612 0.4375567 0.0437046 0.5003265 0.5026959 1.329370 0.6107 0.8308 0.4465358 1.319229 0.0089791 21 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 27 500 5 linear linear 0.6770 0.4563369 0.4345301 0.0218069 0.4997016 0.4985428 1.337639 0.7240 0.8794 0.4392700 1.339596 0.0047400 23 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 28 500 5 linear linear 0.6680 0.4679784 0.4366862 0.0312922 0.5004582 0.4996453 1.328539 0.6493 0.7803 0.4509472 1.327406 0.0142609 25 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 29 500 5 linear linear 0.6430 0.4713631 0.4372131 0.0341500 0.4996292 0.5024753 1.362589 0.6602 0.7550 0.4560737 1.330974 0.0188606 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 30 500 5 linear linear 0.7160 0.4587770 0.4389821 0.0197950 0.5017558 0.5037716 1.330036 0.7435 0.8180 0.4494617 1.320419 0.0104796 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 31 500 5 linear linear 0.6655 0.4933017 0.4361182 0.0571835 0.4991160 0.5001241 1.385708 0.5319 0.7782 0.4518473 1.326849 0.0157291 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 32 500 5 linear linear 0.6720 0.4871688 0.4361993 0.0509695 0.5003332 0.4981529 1.346157 0.5620 0.7804 0.4496812 1.341698 0.0134819 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 33 500 5 linear linear 0.6565 0.4504420 0.4367193 0.0137227 0.4996772 0.5009764 1.353567 0.7849 0.7503 0.4540746 1.361699 0.0173553 27 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 34 500 5 linear linear 0.6375 0.4717427 0.4385073 0.0332354 0.5016662 0.5019757 1.332305 0.6498 0.7754 0.4542480 1.327702 0.0157407 25 0.4 0.6 0.8 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 35 500 5 linear linear 0.6175 0.4417863 0.4354355 0.0063509 0.4999583 0.4977478 1.338536 0.8642 0.9131 0.4380152 1.338737 0.0025797 24 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 36 500 5 linear linear 0.6800 0.4595249 0.4363441 0.0231808 0.4994545 0.4991945 1.342710 0.7104 0.7866 0.4492377 1.335750 0.0128937 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 37 500 5 linear linear 0.6805 0.4774630 0.4375572 0.0399058 0.5003869 0.5011971 1.335900 0.5799 0.7765 0.4521746 1.334852 0.0146174 24 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 38 500 5 linear linear 0.6960 0.4468454 0.4360934 0.0107520 0.5006149 0.5002628 1.329651 0.7890 0.7997 0.4458288 1.322002 0.0097355 22 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 39 500 5 linear linear 0.6635 0.4780945 0.4359979 0.0420966 0.5007337 0.4990710 1.351954 0.6511 0.8364 0.4440418 1.346994 0.0080439 25 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 40 500 5 linear linear 0.6450 0.4509743 0.4356266 0.0153478 0.4990881 0.4990781 1.339484 0.7778 0.8415 0.4431476 1.335253 0.0075211 25 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 41 500 5 linear linear 0.7655 0.4557657 0.4371076 0.0186581 0.5003688 0.5006854 1.322113 0.7726 0.7577 0.4541803 1.317823 0.0170727 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 42 500 5 linear linear 0.6255 0.5009066 0.4356789 0.0652277 0.5009066 0.4984725 1.366479 0.4930 0.7447 0.4548331 1.366641 0.0191542 25 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 43 500 5 linear linear 0.5810 0.4842795 0.4367186 0.0475609 0.4997072 0.4994691 1.348983 0.6029 0.8435 0.4444215 1.320227 0.0077029 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 44 500 5 linear linear 0.6575 0.4540665 0.4365502 0.0175163 0.5001062 0.5008487 1.332364 0.7543 0.8337 0.4452314 1.329723 0.0086812 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 45 500 5 linear linear 0.7210 0.4516133 0.4360591 0.0155542 0.5007416 0.4986059 1.346821 0.7768 0.7418 0.4565726 1.367668 0.0205135 24 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 46 500 5 linear linear 0.7260 0.4736879 0.4379097 0.0357782 0.5010361 0.5024604 1.326122 0.7658 0.8651 0.4435954 1.321763 0.0056857 26 0.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 47 500 5 linear linear 0.7530 0.4704727 0.4364462 0.0340265 0.4990375 0.5004174 1.340801 0.6758 0.7951 0.4497696 1.336621 0.0133234 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 48 500 5 linear linear 0.6225 0.4494387 0.4349172 0.0145215 0.4990628 0.4990973 1.365821 0.7785 0.8070 0.4458279 1.332354 0.0109108 19 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 49 500 5 linear linear 0.7100 0.4773222 0.4353650 0.0419573 0.4985612 0.4991453 1.346859 0.6455 0.7736 0.4504270 1.345287 0.0150620 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 50 500 5 linear linear 0.7385 0.4994877 0.4375697 0.0619180 0.5001597 0.5028319 1.330914 0.5255 0.7667 0.4533889 1.326540 0.0158192 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 51 500 5 linear linear 0.7025 0.4390504 0.4363633 0.0026871 0.4992571 0.5006840 1.333303 0.9112 0.8849 0.4403951 1.331573 0.0040318 22 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 52 500 5 linear linear 0.6830 0.4663237 0.4369236 0.0294001 0.5010887 0.4998892 1.364735 0.6705 0.7490 0.4546459 1.347862 0.0177223 25 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 53 500 5 linear linear 0.6365 0.4791795 0.4383916 0.0407878 0.5011323 0.5019685 1.336873 0.5975 0.7876 0.4520890 1.341745 0.0136974 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 54 500 5 linear linear 0.7240 0.4456748 0.4385689 0.0071060 0.5013474 0.5033336 1.342561 0.8435 0.8574 0.4443452 1.341727 0.0057763 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 55 500 5 linear linear 0.6295 0.4631656 0.4356690 0.0274966 0.4988834 0.4991304 1.360040 0.7294 0.9364 0.4369295 1.319001 0.0012605 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 56 500 5 linear linear 0.6390 0.4667997 0.4366015 0.0301982 0.4993095 0.4997710 1.337446 0.6528 0.8416 0.4443558 1.329503 0.0077543 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 57 500 5 linear linear 0.6115 0.4656425 0.4358885 0.0297539 0.4988914 0.4999767 1.364377 0.6745 0.7079 0.4597613 1.361571 0.0238728 20 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 58 500 5 linear linear 0.6465 0.4464309 0.4376158 0.0088151 0.4999399 0.5012079 1.337730 0.8274 0.9032 0.4404041 1.330838 0.0027883 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 59 500 5 linear linear 0.6895 0.4408958 0.4358108 0.0050850 0.5006488 0.4977516 1.339626 0.8783 0.8789 0.4401138 1.339085 0.0043030 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 60 500 5 linear linear 0.6840 0.4638066 0.4364232 0.0273834 0.5004950 0.4988363 1.322169 0.6672 0.8552 0.4429607 1.320567 0.0065375 24 0.4 0.6 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 61 500 5 linear linear 0.7010 0.4449010 0.4356136 0.0092875 0.5001027 0.4984147 1.331813 0.8005 0.9247 0.4373673 1.327190 0.0017537 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 62 500 5 linear linear 0.6870 0.4838530 0.4358893 0.0479638 0.5006221 0.4987485 1.342710 0.5960 0.7462 0.4546738 1.329965 0.0187845 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 63 500 5 linear linear 0.3810 0.4493408 0.4347091 0.0146317 0.4996076 0.4985158 1.339179 0.7633 0.8904 0.4388159 1.333710 0.0041068 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 64 500 5 linear linear 0.6775 0.4486611 0.4373096 0.0113515 0.5006099 0.5010715 1.335890 0.8128 0.8566 0.4437643 1.328100 0.0064548 26 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 65 500 5 linear linear 0.6705 0.4434602 0.4357118 0.0077484 0.4995031 0.5001215 1.339902 0.9218 0.9289 0.4373779 1.338860 0.0016661 22 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 66 500 5 linear linear 0.5920 0.4728920 0.4376505 0.0352414 0.5010809 0.5002608 1.332050 0.6431 0.8153 0.4478294 1.329763 0.0101788 21 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 67 500 5 linear linear 0.7150 0.4453455 0.4357565 0.0095890 0.4989631 0.4993569 1.340952 0.8255 0.8328 0.4441399 1.339742 0.0083834 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 68 500 5 linear linear 0.6230 0.4837443 0.4364529 0.0472914 0.4984880 0.5013761 1.347501 0.6071 0.9141 0.4385554 1.332755 0.0021025 22 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 69 500 5 linear linear 0.6500 0.4623068 0.4386796 0.0236272 0.5009079 0.5030881 1.350066 0.7379 0.8316 0.4475299 1.337586 0.0088503 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 70 500 5 linear linear 0.6390 0.4537424 0.4385320 0.0152104 0.5019627 0.5018487 1.341086 0.7950 0.8893 0.4423617 1.318175 0.0038298 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 71 500 5 linear linear 0.7000 0.4793636 0.4361384 0.0432252 0.4998332 0.5002308 1.345212 0.5949 0.8851 0.4402734 1.342231 0.0041350 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 72 500 5 linear linear 0.5675 0.4432711 0.4347942 0.0084769 0.4995845 0.4977567 1.338176 0.8466 0.8893 0.4385120 1.351922 0.0037178 21 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 73 500 5 linear linear 0.7420 0.4669342 0.4380090 0.0289252 0.5006180 0.5016093 1.349520 0.7254 0.7856 0.4519981 1.345994 0.0139891 28 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 74 500 5 linear linear 0.7240 0.4618363 0.4362623 0.0255740 0.5008588 0.4986158 1.348365 0.6561 0.8821 0.4407168 1.360309 0.0044544 23 0.2 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 75 500 5 linear linear 0.5675 0.4591938 0.4359650 0.0232287 0.5001320 0.4999661 1.348723 0.7252 0.7869 0.4498856 1.326761 0.0139206 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 76 500 5 linear linear 0.7460 0.4526026 0.4359077 0.0166949 0.4988953 0.4999511 1.334023 0.7659 0.8167 0.4465110 1.331893 0.0106033 21 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 77 500 5 linear linear 0.7430 0.4499433 0.4350612 0.0148821 0.4995024 0.4987660 1.338243 0.7916 0.8060 0.4471764 1.321227 0.0121152 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 78 500 5 linear linear 0.5875 0.4501799 0.4384858 0.0116941 0.5009556 0.5018853 1.335272 0.8095 0.8476 0.4455017 1.316517 0.0070160 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 79 500 5 linear linear 0.6255 0.4403244 0.4350345 0.0052899 0.4995926 0.4978340 1.329610 0.8643 0.8981 0.4380980 1.322116 0.0030635 20 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 80 500 5 linear linear 0.3850 0.5009181 0.4379887 0.0629293 0.5005006 0.5014022 1.363030 0.5053 0.6925 0.4653963 1.357565 0.0274076 24 0.2 0.4 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 81 500 5 linear linear 0.6475 0.4854798 0.4368149 0.0486649 0.4989327 0.5005275 1.370462 0.5758 0.7618 0.4546229 1.358836 0.0178080 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 82 500 5 linear linear 0.6080 0.4475700 0.4355371 0.0120329 0.4998828 0.4986613 1.351719 0.8498 0.8492 0.4420360 1.326205 0.0064989 24 0.2 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 83 500 5 linear linear 0.7205 0.4546266 0.4356279 0.0189987 0.4990759 0.5003110 1.329091 0.8277 0.9121 0.4383431 1.326405 0.0027152 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 84 500 5 linear linear 0.6430 0.4572465 0.4370662 0.0201803 0.5004448 0.5008281 1.340718 0.8085 0.8911 0.4407501 1.347256 0.0036839 24 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 85 500 5 linear linear 0.6150 0.4430765 0.4371091 0.0059674 0.4999200 0.5024723 1.356508 0.8625 0.8887 0.4410242 1.351570 0.0039151 28 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 86 500 5 linear linear 0.6670 0.4621508 0.4355492 0.0266016 0.5001553 0.4987341 1.349729 0.6928 0.9093 0.4382697 1.342414 0.0027205 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 87 500 5 linear linear 0.7380 0.5033225 0.4367895 0.0665330 0.5001588 0.5001620 1.329935 0.4931 0.8172 0.4463577 1.317424 0.0095682 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 88 500 5 linear linear 0.6915 0.4498516 0.4365671 0.0132845 0.5007190 0.5000496 1.336909 0.8019 0.7834 0.4517850 1.347036 0.0152179 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 89 500 5 linear linear 0.5685 0.4665431 0.4344526 0.0320905 0.5003366 0.4953226 1.339960 0.6490 0.8240 0.4439923 1.335224 0.0095397 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 90 500 5 linear linear 0.6960 0.4481007 0.4373883 0.0107124 0.5002283 0.5011747 1.336185 0.8022 0.9023 0.4402918 1.346192 0.0029035 23 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 91 500 5 linear linear 0.7710 0.4563550 0.4363023 0.0200527 0.4999161 0.4990632 1.347949 0.8212 0.8504 0.4433801 1.339494 0.0070778 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 92 500 5 linear linear 0.6080 0.4800464 0.4365127 0.0435336 0.4998066 0.4996887 1.333560 0.4985 0.8730 0.4417706 1.338924 0.0052579 26 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 93 500 5 linear linear 0.6200 0.4793211 0.4367023 0.0426188 0.4996410 0.4985228 1.345770 0.5695 0.7899 0.4500204 1.341541 0.0133180 22 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 94 500 5 linear linear 0.6680 0.4476466 0.4355752 0.0120714 0.4993203 0.4990633 1.325878 0.7821 0.9314 0.4369810 1.325069 0.0014058 23 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 95 500 5 linear linear 0.7085 0.4582352 0.4362554 0.0219798 0.4997999 0.4994685 1.339333 0.7317 0.7082 0.4600530 1.341496 0.0237976 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 96 500 5 linear linear 0.6645 0.4783374 0.4363838 0.0419536 0.4989213 0.5008380 1.353663 0.6094 0.7609 0.4534535 1.340521 0.0170697 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 97 500 5 linear linear 0.7205 0.4422780 0.4346043 0.0076737 0.4982770 0.4986613 1.345841 0.8500 0.9243 0.4365065 1.331605 0.0019022 21 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 98 500 5 linear linear 0.7170 0.4500157 0.4356055 0.0144102 0.4992108 0.4980434 1.334108 0.7850 0.7846 0.4493315 1.331907 0.0137260 20 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 99 500 5 linear linear 0.7235 0.4508100 0.4360086 0.0148014 0.4999917 0.4990490 1.342678 0.8039 0.8072 0.4472968 1.337882 0.0112882 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
1 100 500 5 linear linear 0.6385 0.4414545 0.4365999 0.0048547 0.5011059 0.4996570 1.341270 0.8654 0.9281 0.4382614 1.348400 0.0016615 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=linear
2 1 1000 5 linear linear 0.3870 0.4663279 0.4383207 0.0280072 0.5013437 0.5026584 1.331007 0.6826 0.8105 0.4497991 1.327244 0.0114784 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 2 1000 5 linear linear 0.6270 0.4419337 0.4370122 0.0049215 0.5005415 0.5020637 1.315284 0.8873 0.8832 0.4413299 1.309114 0.0043177 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 3 1000 5 linear linear 0.6940 0.4478715 0.4356069 0.0122646 0.4995947 0.4990943 1.338243 0.7965 0.8763 0.4405199 1.335745 0.0049130 25 0.4 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 4 1000 5 linear linear 0.7340 0.4369190 0.4346641 0.0022549 0.4982419 0.4982635 1.331735 0.9186 0.9153 0.4370933 1.322738 0.0024292 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 5 1000 5 linear linear 0.6035 0.4742783 0.4395859 0.0346924 0.5005727 0.5046144 1.322251 0.5975 0.8275 0.4486409 1.318618 0.0090550 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 6 1000 5 linear linear 0.6905 0.4734058 0.4359164 0.0374894 0.5011270 0.4987275 1.321270 0.7012 0.8501 0.4432135 1.315821 0.0072970 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 7 1000 5 linear linear 0.6660 0.4752811 0.4357628 0.0395183 0.4982078 0.5000556 1.342451 0.6402 0.8825 0.4396658 1.323113 0.0039030 25 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 8 1000 5 linear linear 0.6450 0.4631770 0.4371422 0.0260347 0.5020839 0.4995733 1.327360 0.6644 0.8925 0.4406177 1.322706 0.0034754 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 9 1000 5 linear linear 0.6420 0.4386133 0.4368816 0.0017316 0.5010072 0.4995984 1.325810 0.9258 0.9134 0.4392092 1.322979 0.0023276 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 10 1000 5 linear linear 0.7410 0.4664046 0.4381337 0.0282709 0.5000410 0.5029603 1.318921 0.6793 0.8385 0.4458648 1.326969 0.0077311 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 11 1000 5 linear linear 0.7200 0.4445407 0.4364541 0.0080865 0.4997075 0.5008821 1.330989 0.8517 0.8483 0.4439689 1.326586 0.0075147 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 12 1000 5 linear linear 0.6345 0.4487723 0.4376145 0.0111578 0.5006928 0.5024800 1.334317 0.8131 0.8435 0.4452959 1.332756 0.0076814 27 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 13 1000 5 linear linear 0.6255 0.4575057 0.4369106 0.0205951 0.5015310 0.5002228 1.329022 0.7321 0.8846 0.4412203 1.319765 0.0043097 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 14 1000 5 linear linear 0.6725 0.4566830 0.4359431 0.0207399 0.4985594 0.4996028 1.326404 0.7737 0.8858 0.4396789 1.319478 0.0037358 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 15 1000 5 linear linear 0.6560 0.4592026 0.4366940 0.0225086 0.5009453 0.5012284 1.329928 0.7177 0.8298 0.4462634 1.330697 0.0095694 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 16 1000 5 linear linear 0.6485 0.4571417 0.4345789 0.0225628 0.4999968 0.4965638 1.330920 0.7215 0.8091 0.4465052 1.320843 0.0119262 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 17 1000 5 linear linear 0.6685 0.4566832 0.4380398 0.0186433 0.4999059 0.5018255 1.328561 0.7508 0.8233 0.4469415 1.322196 0.0089016 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 18 1000 5 linear linear 0.6350 0.4731921 0.4365588 0.0366333 0.4996134 0.5008038 1.339192 0.6350 0.8135 0.4476549 1.328902 0.0110960 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 19 1000 5 linear linear 0.7550 0.4411723 0.4356857 0.0054866 0.4978796 0.4992229 1.326610 0.8635 0.9017 0.4385101 1.318382 0.0028244 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 20 1000 5 linear linear 0.6960 0.4550689 0.4361340 0.0189349 0.5009662 0.4999558 1.325592 0.7131 0.9030 0.4393606 1.317229 0.0032267 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 21 1000 5 linear linear 0.6250 0.4451927 0.4370672 0.0081255 0.4992422 0.5008730 1.327927 0.8342 0.7970 0.4477689 1.327317 0.0107018 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 22 1000 5 linear linear 0.7210 0.4489961 0.4361065 0.0128896 0.5009037 0.4991086 1.323007 0.7860 0.8696 0.4416029 1.323256 0.0054964 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 23 1000 5 linear linear 0.6940 0.4525370 0.4365652 0.0159718 0.4999044 0.5007229 1.336130 0.7573 0.8290 0.4452174 1.330590 0.0086522 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 24 1000 5 linear linear 0.6780 0.4442500 0.4362617 0.0079884 0.4996761 0.5006621 1.334176 0.8473 0.8860 0.4406218 1.331051 0.0043602 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 25 1000 5 linear linear 0.7170 0.4648459 0.4359732 0.0288727 0.4987079 0.4995601 1.318831 0.6543 0.9237 0.4379457 1.312942 0.0019725 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 26 1000 5 linear linear 0.7330 0.4547953 0.4365444 0.0182509 0.5012395 0.5000153 1.328007 0.7555 0.8291 0.4458523 1.321395 0.0093080 29 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 27 1000 5 linear linear 0.7110 0.4428123 0.4382493 0.0045630 0.5002867 0.5013663 1.336205 0.8799 0.8676 0.4431699 1.331662 0.0049207 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 28 1000 5 linear linear 0.7375 0.4679179 0.4364758 0.0314422 0.5003895 0.4989161 1.339701 0.6632 0.7952 0.4487782 1.333486 0.0123024 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 29 1000 5 linear linear 0.6980 0.4502011 0.4363677 0.0138334 0.4991039 0.5018744 1.314287 0.7777 0.8953 0.4401460 1.312230 0.0037783 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 30 1000 5 linear linear 0.6860 0.4394601 0.4359226 0.0035375 0.4986259 0.4990507 1.324491 0.8975 0.9066 0.4387613 1.320233 0.0028387 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 31 1000 5 linear linear 0.7585 0.4639647 0.4360211 0.0279435 0.4998917 0.4991997 1.326604 0.7814 0.8633 0.4417931 1.320843 0.0057720 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 32 1000 5 linear linear 0.7330 0.4475897 0.4370641 0.0105257 0.4999355 0.5007408 1.335664 0.8179 0.8154 0.4480129 1.332074 0.0109489 27 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 33 1000 5 linear linear 0.7005 0.4509452 0.4354257 0.0155196 0.4992457 0.4994693 1.330534 0.7771 0.7822 0.4490279 1.328360 0.0136022 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 34 1000 5 linear linear 0.7505 0.4375688 0.4338121 0.0037567 0.4976834 0.4964380 1.323468 0.8884 0.9479 0.4347202 1.318376 0.0009081 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 35 1000 5 linear linear 0.6505 0.4380829 0.4358369 0.0022460 0.4996954 0.4985737 1.321410 0.9178 0.9167 0.4379435 1.317573 0.0021066 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 36 1000 5 linear linear 0.6840 0.4479300 0.4356268 0.0123032 0.4994760 0.4992668 1.339851 0.8044 0.8537 0.4422014 1.333503 0.0065746 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 37 1000 5 linear linear 0.7020 0.4659866 0.4362731 0.0297136 0.4994774 0.5006054 1.325893 0.6442 0.9537 0.4369709 1.320326 0.0006979 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 38 1000 5 linear linear 0.7420 0.4474062 0.4379929 0.0094134 0.4998190 0.5021146 1.336262 0.8287 0.8356 0.4463789 1.329880 0.0083861 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 39 1000 5 linear linear 0.7200 0.4374909 0.4370450 0.0004458 0.5016658 0.5004655 1.336270 0.9638 0.8913 0.4397088 1.330640 0.0026638 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 40 1000 5 linear linear 0.7030 0.4542677 0.4362126 0.0180551 0.4997196 0.5012056 1.336565 0.7560 0.8418 0.4444681 1.326036 0.0082556 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 41 1000 5 linear linear 0.6960 0.4520279 0.4360878 0.0159401 0.4984017 0.5012308 1.320308 0.7663 0.8021 0.4472629 1.313578 0.0111751 32 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 42 1000 5 linear linear 0.6985 0.4398969 0.4372640 0.0026328 0.5008039 0.5007732 1.323325 0.9103 0.9013 0.4400886 1.320424 0.0028245 29 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 43 1000 5 linear linear 0.5860 0.4614545 0.4353054 0.0261490 0.4988866 0.4974581 1.326731 0.6996 0.9259 0.4370818 1.320365 0.0017764 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 44 1000 5 linear linear 0.6925 0.4456797 0.4359087 0.0097709 0.4992611 0.4992202 1.323211 0.8044 0.8803 0.4398220 1.317314 0.0039132 29 0.2 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 45 1000 5 linear linear 0.6805 0.4510206 0.4380241 0.0129965 0.5013060 0.5019393 1.323771 0.7971 0.8306 0.4474692 1.322394 0.0094452 25 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 46 1000 5 linear linear 0.6905 0.4403136 0.4365133 0.0038002 0.5000567 0.5005883 1.332877 0.8892 0.9142 0.4387587 1.326811 0.0022454 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 47 1000 5 linear linear 0.6995 0.4360913 0.4356436 0.0004477 0.5005133 0.4987101 1.316523 0.9713 0.9395 0.4366404 1.314546 0.0009968 27 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 48 1000 5 linear linear 0.7150 0.4626894 0.4374899 0.0251995 0.5011746 0.5001006 1.329797 0.7906 0.8694 0.4417966 1.324365 0.0043067 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 49 1000 5 linear linear 0.7160 0.4747631 0.4374821 0.0372810 0.5008672 0.5011011 1.330223 0.7001 0.8140 0.4478092 1.321147 0.0103271 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 50 1000 5 linear linear 0.6605 0.4530946 0.4378823 0.0152122 0.5005380 0.5014919 1.326612 0.7810 0.8708 0.4429353 1.318863 0.0050530 25 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 51 1000 5 linear linear 0.6405 0.4665700 0.4364223 0.0301476 0.5002949 0.5004278 1.349565 0.6726 0.9445 0.4374922 1.322312 0.0010699 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 52 1000 5 linear linear 0.6085 0.4668666 0.4352176 0.0316490 0.4988021 0.4978445 1.332585 0.6536 0.8493 0.4420371 1.326151 0.0068195 28 0.2 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 53 1000 5 linear linear 0.7535 0.4443262 0.4383370 0.0059892 0.5019470 0.5016532 1.335145 0.8651 0.8643 0.4441755 1.327133 0.0058384 28 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 54 1000 5 linear linear 0.7105 0.4501221 0.4369196 0.0132025 0.4994792 0.5014287 1.328117 0.7731 0.8971 0.4401886 1.327073 0.0032690 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 55 1000 5 linear linear 0.7155 0.4631774 0.4354089 0.0277684 0.5002685 0.4974791 1.327812 0.8276 0.9030 0.4381884 1.326104 0.0027794 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 56 1000 5 linear linear 0.7370 0.4567251 0.4367931 0.0199321 0.4999430 0.5004485 1.324023 0.7430 0.8308 0.4457956 1.319451 0.0090026 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 57 1000 5 linear linear 0.5590 0.4550429 0.4371007 0.0179422 0.4997778 0.5024388 1.328224 0.7306 0.9014 0.4401156 1.326027 0.0030149 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 58 1000 5 linear linear 0.6820 0.4431709 0.4356724 0.0074985 0.4997813 0.4997035 1.326757 0.8558 0.8537 0.4422980 1.324623 0.0066256 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 59 1000 5 linear linear 0.5735 0.4575312 0.4357488 0.0217824 0.4998122 0.5001626 1.326688 0.7277 0.8268 0.4456950 1.322920 0.0099462 28 0.4 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 60 1000 5 linear linear 0.6235 0.4624779 0.4364306 0.0260473 0.5006743 0.4988448 1.322525 0.6900 0.8937 0.4399389 1.314265 0.0035084 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 61 1000 5 linear linear 0.7000 0.4519379 0.4383880 0.0135499 0.5014882 0.5019637 1.336578 0.7894 0.8133 0.4491536 1.331643 0.0107655 25 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 62 1000 5 linear linear 0.7140 0.4524150 0.4355168 0.0168981 0.4996714 0.4983927 1.330882 0.7665 0.8209 0.4458545 1.324530 0.0103376 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 63 1000 5 linear linear 0.7120 0.4499554 0.4373631 0.0125923 0.5003065 0.5020680 1.329144 0.7879 0.8537 0.4439008 1.327144 0.0065377 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 64 1000 5 linear linear 0.6875 0.4453339 0.4365521 0.0087819 0.4998464 0.5010886 1.315602 0.8149 0.9019 0.4397327 1.314059 0.0031806 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 65 1000 5 linear linear 0.5975 0.4469080 0.4367552 0.0101528 0.4987900 0.5008777 1.335155 0.8157 0.8377 0.4446587 1.344838 0.0079035 23 0.4 0.6 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 66 1000 5 linear linear 0.6500 0.4449992 0.4369919 0.0080073 0.5001557 0.5002784 1.321874 0.8200 0.9490 0.4378489 1.320644 0.0008569 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 67 1000 5 linear linear 0.6480 0.4439512 0.4355214 0.0084298 0.5005092 0.4974955 1.322032 0.8443 0.8828 0.4397523 1.320258 0.0042308 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 68 1000 5 linear linear 0.7275 0.4603167 0.4349744 0.0253423 0.5004989 0.4993714 1.338368 0.7525 0.8855 0.4389749 1.316919 0.0040005 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 69 1000 5 linear linear 0.6000 0.4621896 0.4377055 0.0244842 0.5001949 0.5019225 1.336526 0.6777 0.9261 0.4393088 1.322404 0.0016034 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 70 1000 5 linear linear 0.7060 0.4406707 0.4368481 0.0038225 0.4989421 0.5003813 1.324434 0.8887 0.9068 0.4393991 1.324857 0.0025509 27 0.4 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 71 1000 5 linear linear 0.7230 0.4553655 0.4367227 0.0186428 0.5006046 0.4989672 1.315361 0.7289 0.8428 0.4446291 1.314153 0.0079063 27 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 72 1000 5 linear linear 0.5900 0.4803798 0.4383029 0.0420770 0.5024530 0.5008331 1.332552 0.6146 0.7385 0.4593146 1.321821 0.0210117 27 0.4 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 73 1000 5 linear linear 0.6540 0.4621529 0.4363146 0.0258383 0.4989598 0.5005019 1.327850 0.6569 0.9063 0.4390557 1.330925 0.0027411 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 74 1000 5 linear linear 0.7205 0.4634788 0.4365992 0.0268796 0.5007356 0.4998865 1.338220 0.6927 0.7691 0.4530745 1.331742 0.0164754 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 75 1000 5 linear linear 0.7345 0.4460132 0.4370686 0.0089446 0.5003173 0.5002587 1.335362 0.8328 0.8557 0.4436050 1.325597 0.0065364 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 76 1000 5 linear linear 0.7170 0.4779436 0.4343269 0.0436167 0.4987213 0.4971690 1.348561 0.5795 0.8871 0.4379702 1.326967 0.0036434 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 77 1000 5 linear linear 0.7205 0.4443431 0.4371432 0.0071999 0.5011467 0.4995152 1.327371 0.8565 0.8509 0.4440833 1.324400 0.0069401 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 78 1000 5 linear linear 0.6945 0.4449284 0.4353470 0.0095813 0.4994296 0.4984465 1.335466 0.8194 0.9111 0.4378935 1.326959 0.0025465 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 79 1000 5 linear linear 0.6750 0.4470305 0.4380934 0.0089371 0.5000330 0.5031887 1.325778 0.8317 0.8410 0.4461011 1.327001 0.0080078 24 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 80 1000 5 linear linear 0.6945 0.4436190 0.4341449 0.0094741 0.4980515 0.4966170 1.323711 0.8246 0.8780 0.4387878 1.321405 0.0046429 32 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 81 1000 5 linear linear 0.7260 0.4532466 0.4385036 0.0147430 0.5012135 0.5026610 1.323273 0.7701 0.8617 0.4446401 1.318871 0.0061365 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 82 1000 5 linear linear 0.6780 0.4417945 0.4363782 0.0054163 0.5007912 0.4996160 1.333006 0.8791 0.8977 0.4397115 1.336008 0.0033333 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 83 1000 5 linear linear 0.5775 0.4489855 0.4351403 0.0138452 0.5009427 0.4978223 1.329327 0.8505 0.8291 0.4446003 1.324590 0.0094600 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 84 1000 5 linear linear 0.7350 0.4458516 0.4365816 0.0092700 0.5007264 0.4999182 1.327723 0.8333 0.8254 0.4459323 1.321048 0.0093507 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 85 1000 5 linear linear 0.5975 0.4615797 0.4377821 0.0237975 0.5023072 0.5011474 1.340509 0.6975 0.7699 0.4520179 1.338645 0.0142357 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 86 1000 5 linear linear 0.7370 0.4517282 0.4353544 0.0163738 0.4982521 0.4994726 1.325473 0.7674 0.8608 0.4415468 1.322305 0.0061924 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 87 1000 5 linear linear 0.6825 0.4596396 0.4372529 0.0223867 0.5011163 0.5013242 1.339782 0.6989 0.8733 0.4422800 1.330548 0.0050271 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 88 1000 5 linear linear 0.6275 0.4582182 0.4364401 0.0217781 0.5001841 0.5000516 1.333881 0.7047 0.8934 0.4402154 1.329444 0.0037753 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 89 1000 5 linear linear 0.7030 0.4380977 0.4368397 0.0012580 0.5012278 0.5003301 1.329422 0.9378 0.9201 0.4388569 1.331998 0.0020172 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 90 1000 5 linear linear 0.6300 0.4498401 0.4364594 0.0133807 0.4999584 0.5001028 1.334019 0.8147 0.8261 0.4449208 1.322474 0.0084614 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 91 1000 5 linear linear 0.7125 0.4443463 0.4359178 0.0084286 0.4995858 0.4997508 1.309761 0.8365 0.8265 0.4450295 1.309697 0.0091117 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 92 1000 5 linear linear 0.6435 0.4695487 0.4361690 0.0333797 0.5001344 0.5006329 1.335036 0.6510 0.9124 0.4385311 1.312920 0.0023620 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 93 1000 5 linear linear 0.7360 0.4386265 0.4362469 0.0023796 0.5000427 0.5000829 1.324555 0.9195 0.8925 0.4399399 1.318870 0.0036930 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 94 1000 5 linear linear 0.6485 0.4649254 0.4366577 0.0282677 0.5006884 0.5008123 1.341285 0.7795 0.8978 0.4401806 1.333499 0.0035229 28 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 95 1000 5 linear linear 0.7190 0.4549939 0.4355573 0.0194367 0.4998208 0.4990219 1.317548 0.7203 0.9029 0.4385686 1.311983 0.0030113 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 96 1000 5 linear linear 0.6595 0.4455581 0.4372251 0.0083329 0.5001802 0.5024043 1.323739 0.8338 0.8650 0.4431855 1.328196 0.0059604 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 97 1000 5 linear linear 0.4660 0.4621450 0.4368455 0.0252995 0.5003759 0.5009030 1.329897 0.7036 0.8714 0.4422920 1.316564 0.0054465 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 98 1000 5 linear linear 0.7355 0.4447643 0.4371303 0.0076339 0.5006928 0.5005350 1.329373 0.8406 0.8566 0.4432078 1.326492 0.0060775 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 99 1000 5 linear linear 0.5750 0.4583748 0.4366714 0.0217034 0.5008374 0.5007121 1.325532 0.7699 0.8766 0.4416692 1.320759 0.0049977 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
2 100 1000 5 linear linear 0.6870 0.4521714 0.4358405 0.0163309 0.5003074 0.4978629 1.331492 0.7818 0.8554 0.4424719 1.329501 0.0066315 36 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=linear
3 1 2000 5 linear linear 0.7230 0.4425578 0.4372708 0.0052869 0.5004265 0.5012314 1.312720 0.8650 0.8876 0.4412021 1.311459 0.0039312 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 2 2000 5 linear linear 0.6840 0.4467487 0.4384100 0.0083387 0.5034208 0.5022415 1.312614 0.8338 0.9394 0.4396129 1.309168 0.0012029 30 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 3 2000 5 linear linear 0.6890 0.4387924 0.4365608 0.0022316 0.4996566 0.4985471 1.330505 0.9138 0.9702 0.4368635 1.326872 0.0003027 31 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 4 2000 5 linear linear 0.7035 0.4417011 0.4348154 0.0068857 0.4987721 0.4974016 1.319036 0.8550 0.8848 0.4393018 1.315437 0.0044864 32 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 5 2000 5 linear linear 0.5840 0.4452889 0.4385508 0.0067381 0.5028222 0.5006212 1.313791 0.8654 0.8794 0.4430752 1.307809 0.0045244 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 6 2000 5 linear linear 0.7435 0.4407776 0.4358569 0.0049207 0.4993279 0.4998730 1.319823 0.8805 0.8926 0.4393954 1.314221 0.0035385 31 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 7 2000 5 linear linear 0.7185 0.4430097 0.4360659 0.0069438 0.5006121 0.4979725 1.317535 0.8194 0.9183 0.4381779 1.318310 0.0021120 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 8 2000 5 linear linear 0.6550 0.4456432 0.4370811 0.0085621 0.5011657 0.5002374 1.321596 0.8134 0.9080 0.4399996 1.318460 0.0029185 37 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 9 2000 5 linear linear 0.7580 0.4439442 0.4369282 0.0070160 0.5006432 0.5004644 1.323323 0.8355 0.8816 0.4401676 1.320927 0.0032394 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 10 2000 5 linear linear 0.7675 0.4379502 0.4352195 0.0027307 0.4986060 0.4996274 1.322798 0.9071 0.9146 0.4373309 1.319345 0.0021114 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 11 2000 5 linear linear 0.6175 0.4464438 0.4370288 0.0094150 0.5001638 0.4986622 1.321074 0.8183 0.8885 0.4407953 1.321053 0.0037665 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 12 2000 5 linear linear 0.6485 0.4434379 0.4383609 0.0050770 0.5020470 0.5011838 1.323702 0.8845 0.8551 0.4454110 1.325865 0.0070501 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 13 2000 5 linear linear 0.6785 0.4367675 0.4341658 0.0026017 0.4984044 0.4973269 1.317097 0.9140 0.9150 0.4363654 1.315272 0.0021996 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 14 2000 5 linear linear 0.7355 0.4432858 0.4370703 0.0062155 0.5001637 0.5012902 1.320074 0.8583 0.8936 0.4406057 1.317424 0.0035354 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 15 2000 5 linear linear 0.6505 0.4470611 0.4366756 0.0103855 0.4991778 0.5018187 1.329124 0.8228 0.8512 0.4440242 1.326357 0.0073486 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 16 2000 5 linear linear 0.6940 0.4444952 0.4362827 0.0082125 0.4998288 0.4994547 1.321885 0.8399 0.9309 0.4378713 1.310804 0.0015886 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 17 2000 5 linear linear 0.6270 0.4458690 0.4379727 0.0078963 0.5005431 0.5032925 1.320775 0.8395 0.9021 0.4409359 1.318643 0.0029631 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 18 2000 5 linear linear 0.6985 0.4441490 0.4367468 0.0074022 0.5016494 0.4995517 1.318688 0.8305 0.9241 0.4385627 1.312796 0.0018159 35 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 19 2000 5 linear linear 0.7355 0.4417357 0.4380946 0.0036411 0.5007772 0.5028027 1.314426 0.8721 0.9558 0.4386941 1.311546 0.0005994 34 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 20 2000 5 linear linear 0.7460 0.4542835 0.4361021 0.0181814 0.5005243 0.4986671 1.325309 0.7133 0.8760 0.4394619 1.323121 0.0033598 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 21 2000 5 linear linear 0.6615 0.4563324 0.4372642 0.0190682 0.4998164 0.5004876 1.321561 0.7342 0.8745 0.4421964 1.314808 0.0049322 39 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 22 2000 5 linear linear 0.6605 0.4387644 0.4374027 0.0013617 0.5001459 0.5020600 1.318699 0.9376 0.9447 0.4383872 1.316089 0.0009845 37 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 23 2000 5 linear linear 0.6715 0.4507673 0.4346601 0.0161071 0.4992086 0.4982260 1.308647 0.8023 0.9628 0.4351425 1.308377 0.0004824 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 24 2000 5 linear linear 0.6595 0.4410720 0.4348620 0.0062100 0.4991688 0.4974026 1.320592 0.8554 0.9224 0.4368413 1.315492 0.0019793 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 25 2000 5 linear linear 0.6615 0.4393603 0.4367725 0.0025878 0.4997328 0.5004433 1.318121 0.9148 0.9314 0.4382265 1.312836 0.0014540 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 26 2000 5 linear linear 0.6810 0.4408996 0.4339908 0.0069088 0.4994866 0.4965343 1.326027 0.8518 0.8882 0.4378039 1.319390 0.0038131 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 27 2000 5 linear linear 0.6930 0.4475645 0.4361592 0.0114053 0.5000389 0.4982432 1.320232 0.7860 0.8936 0.4397937 1.317987 0.0036345 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 28 2000 5 linear linear 0.6555 0.4630765 0.4364593 0.0266172 0.5006135 0.5002823 1.323141 0.6693 0.8899 0.4403708 1.318793 0.0039116 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 29 2000 5 linear linear 0.7160 0.4439533 0.4370127 0.0069406 0.5003880 0.4987111 1.319773 0.8456 0.9456 0.4379552 1.319508 0.0009425 32 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 30 2000 5 linear linear 0.7150 0.4424881 0.4384340 0.0040541 0.4998602 0.5034966 1.313102 0.8784 0.9206 0.4404420 1.308966 0.0020081 30 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 31 2000 5 linear linear 0.6985 0.4418952 0.4363278 0.0055674 0.5008241 0.4984932 1.320261 0.9042 0.9016 0.4394173 1.319834 0.0030896 38 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 32 2000 5 linear linear 0.7045 0.4421844 0.4362618 0.0059226 0.4992697 0.5006190 1.304670 0.8602 0.8978 0.4394963 1.302130 0.0032345 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 33 2000 5 linear linear 0.6445 0.4388928 0.4372936 0.0015992 0.5008122 0.5024839 1.314892 0.9332 0.9394 0.4384721 1.315525 0.0011786 39 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 34 2000 5 linear linear 0.6950 0.4569741 0.4371936 0.0197805 0.5001894 0.5007382 1.327880 0.7440 0.8825 0.4413812 1.325962 0.0041877 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 35 2000 5 linear linear 0.7315 0.4445772 0.4361168 0.0084605 0.5002105 0.4991080 1.311923 0.8440 0.9229 0.4380305 1.307037 0.0019137 31 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 36 2000 5 linear linear 0.7275 0.4411569 0.4359239 0.0052330 0.5013432 0.4981601 1.324797 0.8623 0.9413 0.4371023 1.321700 0.0011785 36 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 37 2000 5 linear linear 0.6830 0.4535732 0.4371695 0.0164037 0.5002042 0.5005645 1.331481 0.7602 0.9171 0.4395113 1.324329 0.0023418 30 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 38 2000 5 linear linear 0.6750 0.4458779 0.4354974 0.0103805 0.5001615 0.5000844 1.323214 0.8155 0.9085 0.4382145 1.320697 0.0027171 33 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 39 2000 5 linear linear 0.6675 0.4397188 0.4351329 0.0045859 0.4996979 0.4981568 1.321266 0.8696 0.9441 0.4361249 1.317878 0.0009919 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 40 2000 5 linear linear 0.7780 0.4386968 0.4378194 0.0008774 0.5016929 0.5008613 1.325130 0.9473 0.9147 0.4401495 1.313942 0.0023301 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 41 2000 5 linear linear 0.6995 0.4429800 0.4379029 0.0050771 0.4999703 0.5035230 1.322077 0.8572 0.9148 0.4402819 1.321952 0.0023790 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 42 2000 5 linear linear 0.7375 0.4425343 0.4373194 0.0052148 0.4994901 0.5014644 1.325797 0.8611 0.8046 0.4468655 1.324653 0.0095461 29 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 43 2000 5 linear linear 0.7110 0.4435856 0.4357579 0.0078277 0.4997471 0.5002688 1.310929 0.8322 0.8930 0.4395743 1.308284 0.0038164 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 44 2000 5 linear linear 0.6750 0.4411998 0.4375299 0.0036699 0.5008194 0.5026463 1.314640 0.8981 0.9024 0.4406489 1.311679 0.0031190 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 45 2000 5 linear linear 0.6805 0.4431668 0.4362143 0.0069525 0.4985224 0.4991698 1.319460 0.8497 0.8849 0.4403930 1.319413 0.0041787 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 46 2000 5 linear linear 0.5985 0.4526964 0.4355789 0.0171175 0.4992796 0.4994483 1.317246 0.7335 0.9294 0.4371101 1.314438 0.0015312 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 47 2000 5 linear linear 0.7385 0.4378799 0.4369823 0.0008976 0.5002660 0.5008099 1.312763 0.9492 0.9355 0.4381649 1.309304 0.0011826 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 48 2000 5 linear linear 0.6990 0.4434434 0.4354057 0.0080377 0.4990605 0.4991480 1.320393 0.8266 0.8890 0.4392133 1.319657 0.0038076 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 49 2000 5 linear linear 0.6935 0.4611231 0.4370561 0.0240670 0.5003575 0.5010507 1.317812 0.6817 0.8866 0.4411350 1.315816 0.0040789 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 50 2000 5 linear linear 0.7005 0.4622713 0.4361950 0.0260763 0.4999328 0.4996350 1.324303 0.7631 0.8348 0.4447450 1.322375 0.0085500 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 51 2000 5 linear linear 0.7160 0.4552318 0.4361057 0.0191261 0.5011538 0.4988721 1.320449 0.7094 0.8899 0.4391412 1.313434 0.0030355 40 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 52 2000 5 linear linear 0.6865 0.4535951 0.4365561 0.0170390 0.5000989 0.4988349 1.327648 0.7656 0.9378 0.4377281 1.323059 0.0011719 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 53 2000 5 linear linear 0.7040 0.4536282 0.4376707 0.0159575 0.5003612 0.5021936 1.321456 0.7640 0.8980 0.4405211 1.317262 0.0028504 28 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 54 2000 5 linear linear 0.7190 0.4441619 0.4386349 0.0055270 0.4999623 0.5038029 1.325482 0.8546 0.9153 0.4408643 1.327422 0.0022295 37 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 55 2000 5 linear linear 0.7170 0.4364497 0.4342167 0.0022330 0.4970796 0.4993431 1.316869 0.9235 0.9239 0.4361619 1.312572 0.0019452 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 56 2000 5 linear linear 0.5870 0.4449677 0.4357139 0.0092538 0.4991442 0.4997636 1.331997 0.8297 0.8340 0.4442919 1.329681 0.0085780 34 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 57 2000 5 linear linear 0.6675 0.4405339 0.4360320 0.0045018 0.4999526 0.5000886 1.321065 0.8933 0.9419 0.4371305 1.319113 0.0010985 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 58 2000 5 linear linear 0.7255 0.4416421 0.4373485 0.0042935 0.5004242 0.5023938 1.324526 0.8793 0.9187 0.4394851 1.324860 0.0021366 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 59 2000 5 linear linear 0.6780 0.4418270 0.4348940 0.0069330 0.4987650 0.4990902 1.322655 0.8745 0.9130 0.4372230 1.316085 0.0023290 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 60 2000 5 linear linear 0.7005 0.4448178 0.4376226 0.0071952 0.5004870 0.5020655 1.320036 0.8447 0.9030 0.4406670 1.316581 0.0030444 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 61 2000 5 linear linear 0.6635 0.4575819 0.4366480 0.0209339 0.5007762 0.4986314 1.331890 0.7134 0.9008 0.4397252 1.327200 0.0030772 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 62 2000 5 linear linear 0.7275 0.4391595 0.4368900 0.0022695 0.5002688 0.5004090 1.322213 0.9188 0.9103 0.4395219 1.318051 0.0026319 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 63 2000 5 linear linear 0.7140 0.4606220 0.4387650 0.0218570 0.5017860 0.5027250 1.320744 0.7058 0.9205 0.4408623 1.313986 0.0020973 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 64 2000 5 linear linear 0.7300 0.4407884 0.4371063 0.0036820 0.4985884 0.5013308 1.322940 0.8872 0.9148 0.4392343 1.320356 0.0021279 34 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 65 2000 5 linear linear 0.7640 0.4414283 0.4369199 0.0045084 0.5003880 0.4999351 1.318540 0.8815 0.8943 0.4406020 1.313396 0.0036821 37 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 66 2000 5 linear linear 0.5700 0.4397594 0.4358430 0.0039164 0.4989564 0.5011099 1.331835 0.9032 0.9033 0.4390769 1.326141 0.0032338 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 67 2000 5 linear linear 0.6895 0.4414222 0.4366944 0.0047277 0.5005852 0.5006410 1.323136 0.8689 0.9335 0.4381350 1.322865 0.0014406 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 68 2000 5 linear linear 0.7305 0.4437636 0.4368181 0.0069455 0.4999595 0.5013197 1.319484 0.8429 0.8805 0.4409594 1.313379 0.0041413 40 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 69 2000 5 linear linear 0.6410 0.4519500 0.4354827 0.0164673 0.4988683 0.5005808 1.329268 0.7844 0.9074 0.4383110 1.326154 0.0028283 32 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 70 2000 5 linear linear 0.7515 0.4411811 0.4370305 0.0041505 0.4989661 0.5010267 1.321350 0.8798 0.9326 0.4383181 1.317767 0.0012876 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 71 2000 5 linear linear 0.7510 0.4377815 0.4364829 0.0012985 0.5005105 0.4993702 1.324148 0.9452 0.9476 0.4373633 1.319613 0.0008804 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 72 2000 5 linear linear 0.6760 0.4407098 0.4368660 0.0038438 0.5001998 0.5001029 1.322056 0.8880 0.9429 0.4378556 1.322623 0.0009895 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 73 2000 5 linear linear 0.6505 0.4382195 0.4348407 0.0033788 0.5000238 0.4972628 1.318813 0.9027 0.9331 0.4360482 1.311399 0.0012075 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 74 2000 5 linear linear 0.7345 0.4426653 0.4371034 0.0055620 0.4997261 0.5000505 1.319718 0.8567 0.9105 0.4393647 1.319020 0.0022614 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 75 2000 5 linear linear 0.7590 0.4392695 0.4348512 0.0044183 0.5002085 0.4975382 1.320253 0.8849 0.9239 0.4367612 1.314408 0.0019100 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 76 2000 5 linear linear 0.7305 0.4419799 0.4381596 0.0038204 0.5000513 0.5015078 1.321226 0.8943 0.8909 0.4419676 1.321711 0.0038081 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 77 2000 5 linear linear 0.6840 0.4549887 0.4385848 0.0164039 0.5015766 0.5023698 1.329650 0.7502 0.8780 0.4436156 1.328929 0.0050308 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 78 2000 5 linear linear 0.7285 0.4386685 0.4345103 0.0041582 0.5003503 0.4972520 1.312740 0.8897 0.8823 0.4392027 1.308855 0.0046925 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 79 2000 5 linear linear 0.6000 0.4438808 0.4359165 0.0079643 0.5002495 0.4978375 1.321037 0.8314 0.9031 0.4387047 1.316345 0.0027882 38 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 80 2000 5 linear linear 0.7160 0.4399061 0.4353047 0.0046013 0.4997734 0.4987677 1.320505 0.8738 0.8927 0.4385054 1.320047 0.0032006 33 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 81 2000 5 linear linear 0.7270 0.4400431 0.4363733 0.0036698 0.5018611 0.4971896 1.324291 0.8906 0.8526 0.4429265 1.318974 0.0065532 39 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 82 2000 5 linear linear 0.6860 0.4378842 0.4365985 0.0012857 0.5003604 0.4999800 1.311212 0.9344 0.9390 0.4376717 1.305356 0.0010732 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 83 2000 5 linear linear 0.6625 0.4423230 0.4364652 0.0058578 0.4997741 0.5006946 1.330267 0.8666 0.9110 0.4392317 1.327668 0.0027665 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 84 2000 5 linear linear 0.6550 0.4429311 0.4373564 0.0055747 0.5010905 0.5001360 1.322656 0.8673 0.9219 0.4394131 1.316955 0.0020567 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 85 2000 5 linear linear 0.7450 0.4418575 0.4382002 0.0036572 0.5007932 0.5041103 1.326718 0.8953 0.8959 0.4418507 1.324264 0.0036505 32 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 86 2000 5 linear linear 0.5825 0.4398431 0.4360053 0.0038379 0.5002501 0.4991919 1.321961 0.8883 0.9363 0.4372385 1.320210 0.0012333 32 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 87 2000 5 linear linear 0.6995 0.4469905 0.4364495 0.0105410 0.4986343 0.5005285 1.316481 0.7872 0.9091 0.4389842 1.312173 0.0025347 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 88 2000 5 linear linear 0.7095 0.4378969 0.4359299 0.0019670 0.4997156 0.4996991 1.314369 0.9189 0.9328 0.4374316 1.312896 0.0015017 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 89 2000 5 linear linear 0.6855 0.4440394 0.4382917 0.0057477 0.5037404 0.5002974 1.324199 0.8686 0.8989 0.4415982 1.321056 0.0033065 40 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 90 2000 5 linear linear 0.6095 0.4502941 0.4361123 0.0141818 0.5001657 0.4983576 1.321208 0.7870 0.8989 0.4393431 1.315981 0.0032307 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 91 2000 5 linear linear 0.5145 0.4558466 0.4368201 0.0190266 0.4999323 0.5022758 1.327749 0.7487 0.8455 0.4437692 1.323282 0.0069491 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 92 2000 5 linear linear 0.7285 0.4409834 0.4368827 0.0041007 0.5011179 0.5002633 1.319075 0.8822 0.8892 0.4407191 1.318634 0.0038364 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 93 2000 5 linear linear 0.6425 0.4579960 0.4365410 0.0214550 0.5008877 0.4996793 1.327341 0.7199 0.8327 0.4449947 1.323893 0.0084537 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 94 2000 5 linear linear 0.7170 0.4375828 0.4355311 0.0020518 0.4988950 0.4997817 1.317882 0.9206 0.9491 0.4363965 1.312615 0.0008654 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 95 2000 5 linear linear 0.7180 0.4374550 0.4355070 0.0019481 0.4997502 0.4991807 1.321510 0.9232 0.9050 0.4384201 1.318264 0.0029132 34 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 96 2000 5 linear linear 0.6430 0.4400242 0.4353919 0.0046323 0.4994442 0.4981109 1.312590 0.8882 0.9205 0.4373180 1.308924 0.0019261 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 97 2000 5 linear linear 0.6340 0.4493108 0.4356888 0.0136220 0.4998404 0.4978261 1.316793 0.7896 0.8391 0.4439676 1.315546 0.0082788 37 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 98 2000 5 linear linear 0.6920 0.4370864 0.4363506 0.0007357 0.4998269 0.4993972 1.318319 0.9591 0.9379 0.4374056 1.313212 0.0010549 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 99 2000 5 linear linear 0.6995 0.4412049 0.4370724 0.0041325 0.5002773 0.5021856 1.315730 0.8881 0.8199 0.4470271 1.312615 0.0099546 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
3 100 2000 5 linear linear 0.7235 0.4387055 0.4352224 0.0034832 0.4977555 0.4991365 1.318508 0.8934 0.9328 0.4365017 1.316933 0.0012793 38 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=linear
4 1 500 10 linear linear 0.4705 0.4841060 0.4369009 0.0472051 0.5004449 0.5005257 1.352411 0.5956 0.7363 0.4581374 1.359956 0.0212365 30 0.4 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 2 500 10 linear linear 0.4670 0.4640353 0.4371728 0.0268625 0.4996870 0.5007010 1.337782 0.6943 0.7718 0.4524144 1.330682 0.0152416 27 0.3 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 3 500 10 linear linear 0.5080 0.4757122 0.4359338 0.0397784 0.4997209 0.5007263 1.364099 0.6311 0.7118 0.4613385 1.368935 0.0254047 27 0.1 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 4 500 10 linear linear 0.5090 0.4575233 0.4355620 0.0219613 0.4998149 0.4991473 1.342055 0.7196 0.7331 0.4559760 1.351858 0.0204140 29 0.6 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 5 500 10 linear linear 0.3785 0.4622885 0.4363874 0.0259011 0.5007223 0.4996671 1.344215 0.7002 0.8252 0.4464071 1.345591 0.0100196 32 0.3 0.7 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 6 500 10 linear linear 0.4630 0.4815407 0.4344018 0.0471389 0.4980185 0.4986606 1.377681 0.5488 0.7832 0.4495972 1.370296 0.0151954 26 0.3 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 7 500 10 linear linear 0.5180 0.4586897 0.4364976 0.0221921 0.5009226 0.4994400 1.335469 0.7250 0.8472 0.4442856 1.336707 0.0077880 23 0.5 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 8 500 10 linear linear 0.4580 0.4461630 0.4365186 0.0096444 0.4992365 0.5007306 1.356449 0.8244 0.8429 0.4441447 1.394014 0.0076262 32 0.1 0.7 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 9 500 10 linear linear 0.5130 0.4651353 0.4365538 0.0285815 0.5016094 0.4997976 1.345127 0.7297 0.8241 0.4466178 1.342656 0.0100640 30 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 10 500 10 linear linear 0.4610 0.4680325 0.4372562 0.0307763 0.5023011 0.5012341 1.358157 0.6845 0.7547 0.4564328 1.345613 0.0191766 30 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 11 500 10 linear linear 0.4525 0.4836544 0.4360495 0.0476048 0.4996840 0.5015714 1.358732 0.5759 0.7888 0.4498221 1.343925 0.0137726 27 0.2 0.6 0.9 0.8571429 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 12 500 10 linear linear 0.4470 0.4691088 0.4377441 0.0313646 0.5010833 0.5017797 1.348170 0.6683 0.7881 0.4515168 1.348939 0.0137727 26 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 13 500 10 linear linear 0.4780 0.4496569 0.4372682 0.0123887 0.5007029 0.5020002 1.333331 0.8112 0.7904 0.4508987 1.338647 0.0136305 27 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 14 500 10 linear linear 0.4595 0.4951287 0.4360887 0.0590400 0.4991244 0.4991689 1.383135 0.5201 0.6318 0.4713644 1.380692 0.0352756 23 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 15 500 10 linear linear 0.5650 0.4781382 0.4350993 0.0430389 0.4997875 0.4988608 1.378677 0.6155 0.7124 0.4595965 1.384103 0.0244972 29 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 16 500 10 linear linear 0.5085 0.4848340 0.4369504 0.0478836 0.5011647 0.5013153 1.384039 0.5836 0.6559 0.4710491 1.387463 0.0340987 29 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 17 500 10 linear linear 0.4435 0.4971912 0.4355985 0.0615927 0.4999172 0.5005840 1.376855 0.5168 0.7041 0.4630187 1.350417 0.0274202 27 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 18 500 10 linear linear 0.5325 0.4549136 0.4345908 0.0203228 0.4987287 0.4987000 1.328175 0.7361 0.8412 0.4422106 1.332619 0.0076198 30 0.2 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 19 500 10 linear linear 0.5385 0.4534886 0.4367177 0.0167709 0.5019864 0.4996835 1.350792 0.7701 0.7941 0.4500529 1.370839 0.0133352 27 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 20 500 10 linear linear 0.4380 0.5045378 0.4355615 0.0689763 0.4992989 0.5000072 1.389126 0.4639 0.8118 0.4463648 1.368576 0.0108034 26 0.3 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 21 500 10 linear linear 0.5670 0.4663122 0.4353443 0.0309679 0.5016154 0.4969923 1.356479 0.6751 0.7389 0.4562545 1.350372 0.0209102 28 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 22 500 10 linear linear 0.4720 0.4610789 0.4371744 0.0239045 0.4996626 0.5019996 1.342877 0.7261 0.7103 0.4628067 1.375758 0.0256323 24 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 23 500 10 linear linear 0.5640 0.4872155 0.4362523 0.0509632 0.4997497 0.5010085 1.353530 0.5879 0.7272 0.4584553 1.370716 0.0222030 29 0.2 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 24 500 10 linear linear 0.4685 0.4663817 0.4369228 0.0294590 0.4992845 0.5017445 1.365681 0.6807 0.7571 0.4550344 1.362365 0.0181116 25 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 25 500 10 linear linear 0.4800 0.4491726 0.4370213 0.0121512 0.5018738 0.5005673 1.364111 0.8140 0.8090 0.4488789 1.372412 0.0118576 26 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 26 500 10 linear linear 0.4475 0.4720470 0.4375747 0.0344723 0.5009906 0.5006366 1.352780 0.6465 0.7831 0.4522998 1.352670 0.0147251 29 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 27 500 10 linear linear 0.4575 0.4471129 0.4346809 0.0124320 0.4997529 0.4985988 1.329682 0.8061 0.8346 0.4434133 1.344134 0.0087324 26 0.3 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 28 500 10 linear linear 0.5730 0.4597720 0.4336019 0.0261701 0.4989298 0.4964979 1.343260 0.6974 0.7766 0.4489903 1.350740 0.0153883 29 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 29 500 10 linear linear 0.1995 0.4964248 0.4356202 0.0608046 0.4999034 0.4999188 1.392851 0.5278 0.6771 0.4666600 1.376158 0.0310398 32 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 30 500 10 linear linear 0.6025 0.4721421 0.4347778 0.0373643 0.4994532 0.4972245 1.353963 0.6525 0.7580 0.4527006 1.347705 0.0179228 30 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 31 500 10 linear linear 0.4510 0.4649860 0.4345280 0.0304580 0.5001827 0.4971717 1.349959 0.6840 0.7456 0.4539688 1.365948 0.0194408 26 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 32 500 10 linear linear 0.5835 0.4460760 0.4376238 0.0084523 0.5016910 0.5002429 1.362793 0.8362 0.8088 0.4486556 1.362724 0.0110319 28 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 33 500 10 linear linear 0.4765 0.4740852 0.4364971 0.0375882 0.4987661 0.5006862 1.379945 0.6372 0.7198 0.4597709 1.365940 0.0232738 29 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 34 500 10 linear linear 0.4680 0.4970900 0.4375875 0.0595026 0.5014242 0.5015351 1.371968 0.5183 0.7103 0.4615274 1.361887 0.0239399 28 0.4 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 35 500 10 linear linear 0.3665 0.4744503 0.4354286 0.0390218 0.5005837 0.4978062 1.342264 0.6264 0.7836 0.4486064 1.333692 0.0131778 24 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 36 500 10 linear linear 0.5420 0.4587778 0.4354540 0.0233239 0.4984761 0.4989106 1.343943 0.6971 0.8639 0.4414466 1.356106 0.0059926 25 0.2 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 37 500 10 linear linear 0.3725 0.4658766 0.4347969 0.0310797 0.4979205 0.4993551 1.353785 0.6636 0.7661 0.4517391 1.349351 0.0169422 32 0.3 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 38 500 10 linear linear 0.4585 0.4601385 0.4358753 0.0242632 0.4999237 0.4996569 1.342885 0.7220 0.7298 0.4577117 1.366780 0.0218364 30 0.1 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 39 500 10 linear linear 0.4670 0.4529236 0.4348441 0.0180795 0.4987510 0.4978660 1.341473 0.7526 0.7740 0.4503272 1.364121 0.0154832 24 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 40 500 10 linear linear 0.4015 0.5051609 0.4366123 0.0685486 0.5003578 0.5000963 1.377683 0.4730 0.6992 0.4637419 1.377063 0.0271296 30 0.6 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 41 500 10 linear linear 0.3745 0.4562746 0.4361409 0.0201337 0.5002577 0.5003987 1.357649 0.7423 0.7397 0.4566089 1.360841 0.0204681 27 0.3 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 42 500 10 linear linear 0.5275 0.4913514 0.4362667 0.0550848 0.5001577 0.5007184 1.396571 0.5393 0.7271 0.4588992 1.385172 0.0226325 27 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 43 500 10 linear linear 0.5470 0.4808916 0.4355460 0.0453456 0.4998270 0.4989653 1.348875 0.6210 0.7540 0.4537659 1.343235 0.0182199 30 0.5 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 44 500 10 linear linear 0.5355 0.4434460 0.4348037 0.0086424 0.5000521 0.4982766 1.336187 0.8332 0.7439 0.4547941 1.334985 0.0199904 33 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 45 500 10 linear linear 0.4180 0.4709053 0.4365423 0.0343630 0.5003821 0.4995535 1.373898 0.6532 0.7784 0.4516367 1.351297 0.0150943 19 0.3 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 46 500 10 linear linear 0.5930 0.4690496 0.4369936 0.0320560 0.5002330 0.5021248 1.356541 0.6506 0.8408 0.4449366 1.366745 0.0079429 26 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 47 500 10 linear linear 0.3715 0.4607379 0.4348806 0.0258573 0.4998419 0.4976653 1.353849 0.6840 0.8606 0.4411031 1.357434 0.0062225 26 0.2 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 48 500 10 linear linear 0.5165 0.4629766 0.4362515 0.0267251 0.4993867 0.5001523 1.367782 0.7048 0.7343 0.4581023 1.371654 0.0218507 28 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 49 500 10 linear linear 0.4835 0.4515915 0.4352363 0.0163552 0.5004465 0.4984943 1.348702 0.7777 0.8188 0.4457410 1.346719 0.0105047 24 0.3 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 50 500 10 linear linear 0.3930 0.4601427 0.4336861 0.0264566 0.4979338 0.4976753 1.385581 0.7070 0.7301 0.4562844 1.427197 0.0225982 22 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 51 500 10 linear linear 0.4200 0.4787019 0.4345759 0.0441260 0.4994061 0.4988074 1.368070 0.6246 0.7739 0.4510209 1.380934 0.0164450 28 0.5 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 52 500 10 linear linear 0.4210 0.4630236 0.4361167 0.0269069 0.5008535 0.4993535 1.340164 0.6955 0.8149 0.4470001 1.356355 0.0108833 26 0.1 0.6 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 53 500 10 linear linear 0.4370 0.4710548 0.4347841 0.0362708 0.4982172 0.4991586 1.373677 0.6392 0.8125 0.4461223 1.381373 0.0113383 32 0.5 0.7 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 54 500 10 linear linear 0.5695 0.4626826 0.4350790 0.0276035 0.4988013 0.4985153 1.349461 0.6900 0.7758 0.4507111 1.358333 0.0156321 27 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 55 500 10 linear linear 0.4845 0.4656967 0.4367405 0.0289561 0.5001085 0.4999635 1.359746 0.6801 0.6907 0.4621404 1.362445 0.0253999 32 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 56 500 10 linear linear 0.3215 0.4744345 0.4384915 0.0359429 0.5015850 0.5039179 1.362716 0.6487 0.7624 0.4555150 1.378178 0.0170235 25 0.0 0.4 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 57 500 10 linear linear 0.4455 0.4436325 0.4351595 0.0084730 0.4991179 0.4994175 1.358241 0.8341 0.8252 0.4448627 1.367026 0.0097031 26 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 58 500 10 linear linear 0.4160 0.4856639 0.4370523 0.0486116 0.5002176 0.5005408 1.365909 0.5509 0.7658 0.4536439 1.380408 0.0165916 28 0.1 0.6 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 59 500 10 linear linear 0.5785 0.4486547 0.4347275 0.0139272 0.4989695 0.4983914 1.336479 0.7869 0.8178 0.4454645 1.335456 0.0107370 29 0.3 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 60 500 10 linear linear 0.3575 0.4855616 0.4347851 0.0507765 0.5003071 0.4986661 1.364186 0.5586 0.8331 0.4434456 1.348606 0.0086605 26 0.2 0.7 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 61 500 10 linear linear 0.5060 0.4524129 0.4356798 0.0167331 0.5000245 0.4994696 1.369781 0.7653 0.8246 0.4452797 1.395696 0.0096000 28 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 62 500 10 linear linear 0.3835 0.5247192 0.4371645 0.0875548 0.5000829 0.5001844 1.404985 0.3669 0.7693 0.4531845 1.378611 0.0160200 26 0.1 0.7 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 63 500 10 linear linear 0.4765 0.4947507 0.4346209 0.0601298 0.4998375 0.4980498 1.359435 0.5041 0.8029 0.4468597 1.347409 0.0122388 24 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 64 500 10 linear linear 0.4950 0.4425730 0.4371839 0.0053891 0.5010172 0.5010693 1.337398 0.8719 0.8608 0.4435065 1.359956 0.0063226 30 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 65 500 10 linear linear 0.5245 0.4609964 0.4364567 0.0245397 0.5003097 0.4994075 1.354295 0.7112 0.6943 0.4636182 1.375316 0.0271615 26 0.3 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 66 500 10 linear linear 0.5060 0.5018399 0.4372668 0.0645731 0.5011881 0.5019979 1.354173 0.4790 0.7468 0.4560673 1.370125 0.0188006 27 0.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 67 500 10 linear linear 0.5360 0.4441625 0.4367548 0.0074076 0.5010876 0.4994633 1.347902 0.8424 0.9063 0.4392517 1.338117 0.0024969 23 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 68 500 10 linear linear 0.5230 0.4522556 0.4368236 0.0154321 0.5012092 0.4998057 1.356920 0.7748 0.8176 0.4470443 1.346162 0.0102208 26 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 69 500 10 linear linear 0.5430 0.4652678 0.4338859 0.0313819 0.4995218 0.4965286 1.345416 0.6613 0.7941 0.4475091 1.359453 0.0136232 29 0.5 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 70 500 10 linear linear 0.5830 0.4529971 0.4357343 0.0172627 0.5004021 0.4993863 1.334262 0.7669 0.7925 0.4493562 1.327453 0.0136219 25 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 71 500 10 linear linear 0.4605 0.4819016 0.4354267 0.0464749 0.5010596 0.4984582 1.361835 0.5762 0.7519 0.4539564 1.350171 0.0185297 28 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 72 500 10 linear linear 0.5580 0.4673816 0.4360162 0.0313654 0.4982657 0.5013251 1.356102 0.6779 0.7318 0.4574011 1.358016 0.0213849 29 0.4 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 73 500 10 linear linear 0.4800 0.4832605 0.4353471 0.0479134 0.4992276 0.5000539 1.359452 0.5842 0.6537 0.4706975 1.351538 0.0353504 28 0.3 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 74 500 10 linear linear 0.4970 0.4785127 0.4366099 0.0419028 0.5011801 0.4987701 1.370322 0.6263 0.7232 0.4597732 1.365985 0.0231633 23 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 75 500 10 linear linear 0.3345 0.4780672 0.4368321 0.0412351 0.5006879 0.5012650 1.364446 0.6187 0.7167 0.4615875 1.347101 0.0247554 31 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 76 500 10 linear linear 0.5575 0.4607979 0.4370366 0.0237613 0.5008208 0.5008647 1.344299 0.7158 0.7114 0.4618489 1.350418 0.0248123 29 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 77 500 10 linear linear 0.4030 0.4668452 0.4336362 0.0332090 0.4974900 0.4973551 1.361864 0.6681 0.8209 0.4437936 1.368534 0.0101574 25 0.3 0.6 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 78 500 10 linear linear 0.4520 0.4792824 0.4377340 0.0415484 0.5021455 0.5015555 1.371025 0.6241 0.7174 0.4618215 1.362209 0.0240875 31 0.3 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 79 500 10 linear linear 0.4870 0.4688227 0.4375261 0.0312965 0.5015036 0.5002439 1.348469 0.6616 0.8309 0.4462882 1.361519 0.0087620 25 0.5 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 80 500 10 linear linear 0.4190 0.4585109 0.4373668 0.0211441 0.5005463 0.5011483 1.356541 0.7335 0.7842 0.4517831 1.384303 0.0144163 28 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 81 500 10 linear linear 0.3460 0.4626501 0.4355843 0.0270658 0.4999748 0.4997333 1.351466 0.6873 0.8124 0.4465383 1.363788 0.0109540 32 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 82 500 10 linear linear 0.5260 0.4809129 0.4362316 0.0446813 0.4995394 0.5008715 1.350661 0.5910 0.7509 0.4549571 1.360615 0.0187255 28 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 83 500 10 linear linear 0.4440 0.4742172 0.4383723 0.0358449 0.5014734 0.5032172 1.357154 0.6460 0.7388 0.4592686 1.362687 0.0208963 22 0.2 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 84 500 10 linear linear 0.5470 0.4516843 0.4364078 0.0152766 0.5002521 0.4990506 1.347998 0.7715 0.8133 0.4472661 1.348377 0.0108584 28 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 85 500 10 linear linear 0.3810 0.4907555 0.4376949 0.0530606 0.5006810 0.5017566 1.371523 0.5528 0.7369 0.4589644 1.369017 0.0212695 31 0.2 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 86 500 10 linear linear 0.4530 0.4952140 0.4352028 0.0600112 0.5001806 0.4980607 1.360312 0.5249 0.6969 0.4624319 1.353782 0.0272292 26 0.4 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 87 500 10 linear linear 0.4985 0.5008027 0.4372392 0.0635634 0.4997207 0.5024935 1.374632 0.4279 0.7484 0.4570086 1.394402 0.0197694 28 0.4 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 88 500 10 linear linear 0.5350 0.4585960 0.4360739 0.0225220 0.5011281 0.4990917 1.349778 0.7758 0.8231 0.4459075 1.338709 0.0098335 27 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 89 500 10 linear linear 0.5315 0.4590893 0.4360369 0.0230524 0.4991451 0.5010315 1.340917 0.7201 0.8111 0.4470806 1.348530 0.0110437 29 0.2 0.7 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 90 500 10 linear linear 0.5030 0.4863019 0.4370734 0.0492285 0.5003159 0.5027133 1.352283 0.6076 0.7393 0.4586531 1.381071 0.0215797 29 0.1 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 91 500 10 linear linear 0.5055 0.4536575 0.4367416 0.0169158 0.5004005 0.5015602 1.346055 0.7676 0.7594 0.4543629 1.358279 0.0176213 29 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 92 500 10 linear linear 0.5560 0.4755185 0.4350868 0.0404317 0.4995763 0.4988552 1.341233 0.6355 0.7768 0.4508312 1.352609 0.0157444 23 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 93 500 10 linear linear 0.5285 0.4489044 0.4370509 0.0118535 0.5013808 0.5000038 1.340997 0.8042 0.7948 0.4499475 1.351468 0.0128966 24 0.8 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 94 500 10 linear linear 0.5820 0.4758175 0.4351245 0.0406930 0.4994826 0.5006206 1.349088 0.6411 0.7209 0.4586663 1.345411 0.0235418 26 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 95 500 10 linear linear 0.5180 0.4671966 0.4356674 0.0315292 0.4992112 0.4994894 1.338269 0.7026 0.7761 0.4516099 1.332169 0.0159425 31 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 96 500 10 linear linear 0.4305 0.4485656 0.4350836 0.0134820 0.4979778 0.4984756 1.351510 0.7948 0.7912 0.4479182 1.365523 0.0128346 27 0.2 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 97 500 10 linear linear 0.6070 0.4537454 0.4363066 0.0174387 0.4997406 0.5014604 1.334941 0.7896 0.8333 0.4453347 1.338717 0.0090281 28 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 98 500 10 linear linear 0.5295 0.4498968 0.4357404 0.0141565 0.5000312 0.5003960 1.363487 0.7876 0.7683 0.4524627 1.384580 0.0167223 25 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 99 500 10 linear linear 0.4450 0.4630928 0.4365502 0.0265426 0.5009321 0.5004115 1.357191 0.7019 0.8164 0.4473487 1.349556 0.0107985 25 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
4 100 500 10 linear linear 0.5325 0.4534109 0.4374163 0.0159945 0.5011298 0.5011112 1.341611 0.7707 0.8429 0.4452238 1.342464 0.0078075 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=linear
5 1 1000 10 linear linear 0.4995 0.4607514 0.4370978 0.0236536 0.5015505 0.5005140 1.340437 0.7245 0.8073 0.4490101 1.340559 0.0119123 34 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 2 1000 10 linear linear 0.5245 0.4523237 0.4367465 0.0155772 0.5016180 0.4990087 1.321630 0.7738 0.8237 0.4463375 1.316300 0.0095910 34 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 3 1000 10 linear linear 0.5325 0.4477095 0.4374131 0.0102964 0.5005231 0.5022692 1.341643 0.8256 0.7838 0.4511937 1.334566 0.0137806 33 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 4 1000 10 linear linear 0.5145 0.4670824 0.4340328 0.0330496 0.4983836 0.4986249 1.344293 0.6217 0.8268 0.4436075 1.337482 0.0095747 32 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 5 1000 10 linear linear 0.5350 0.4528186 0.4368133 0.0160053 0.5004539 0.5005936 1.335908 0.7848 0.8143 0.4475811 1.334762 0.0107678 36 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 6 1000 10 linear linear 0.4915 0.4666410 0.4351892 0.0314518 0.4984247 0.5008003 1.359532 0.6634 0.7825 0.4498983 1.347539 0.0147091 33 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 7 1000 10 linear linear 0.5490 0.4423535 0.4347642 0.0075892 0.4993619 0.4981021 1.328896 0.8451 0.8729 0.4397830 1.333044 0.0050188 32 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 8 1000 10 linear linear 0.5840 0.4393902 0.4337693 0.0056209 0.4975671 0.4983210 1.321520 0.8630 0.8478 0.4409222 1.325824 0.0071529 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 9 1000 10 linear linear 0.5195 0.4671157 0.4367218 0.0303940 0.5001103 0.5008231 1.329403 0.7562 0.8260 0.4459484 1.329314 0.0092266 30 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 10 1000 10 linear linear 0.5570 0.4509138 0.4357305 0.0151833 0.5006421 0.5007738 1.326068 0.7745 0.8163 0.4466015 1.324612 0.0108710 33 0.7 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 11 1000 10 linear linear 0.6020 0.4430738 0.4352775 0.0077963 0.4996008 0.4995416 1.333725 0.8399 0.8850 0.4394046 1.336132 0.0041271 40 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 12 1000 10 linear linear 0.5180 0.4553560 0.4351099 0.0202461 0.4987425 0.4999225 1.336043 0.7424 0.7599 0.4519659 1.333309 0.0168560 40 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 13 1000 10 linear linear 0.5820 0.4495149 0.4377526 0.0117623 0.5000023 0.5019995 1.322310 0.7977 0.7612 0.4545210 1.323865 0.0167684 37 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 14 1000 10 linear linear 0.5995 0.4542009 0.4361754 0.0180255 0.5007171 0.5001895 1.336032 0.7588 0.7714 0.4523788 1.344510 0.0162034 31 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 15 1000 10 linear linear 0.4880 0.4449888 0.4374743 0.0075145 0.5015952 0.5001211 1.336147 0.8481 0.8381 0.4455307 1.343703 0.0080564 35 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 16 1000 10 linear linear 0.4820 0.4520190 0.4375078 0.0145113 0.5029523 0.5001327 1.328840 0.7925 0.8149 0.4487481 1.328211 0.0112403 38 0.7 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 17 1000 10 linear linear 0.4865 0.4456586 0.4369360 0.0087227 0.5017830 0.4998707 1.330443 0.8356 0.8632 0.4429625 1.336343 0.0060265 30 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 18 1000 10 linear linear 0.4870 0.4546512 0.4352369 0.0194143 0.5001936 0.4980886 1.352553 0.7442 0.8575 0.4413947 1.336466 0.0061578 38 0.3 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 19 1000 10 linear linear 0.5290 0.4439605 0.4362487 0.0077119 0.4994188 0.5010903 1.332275 0.8488 0.7997 0.4493488 1.333909 0.0131001 39 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 20 1000 10 linear linear 0.4395 0.4555741 0.4366177 0.0189565 0.4990966 0.5028995 1.337626 0.7356 0.8029 0.4485641 1.350092 0.0119465 32 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 21 1000 10 linear linear 0.5310 0.4688744 0.4326641 0.0362103 0.4970813 0.4965589 1.341682 0.6629 0.7901 0.4462652 1.336269 0.0136011 37 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 22 1000 10 linear linear 0.5705 0.4561163 0.4386424 0.0174739 0.5011338 0.5030828 1.326728 0.7505 0.7948 0.4508207 1.322289 0.0121783 39 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 23 1000 10 linear linear 0.4375 0.4666568 0.4354119 0.0312449 0.4997234 0.4990843 1.351908 0.6794 0.8167 0.4458814 1.346097 0.0104695 36 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 24 1000 10 linear linear 0.6010 0.4419105 0.4356055 0.0063051 0.5010946 0.4986767 1.330494 0.8575 0.8691 0.4410752 1.328523 0.0054697 32 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 25 1000 10 linear linear 0.5805 0.4423198 0.4354968 0.0068231 0.5000581 0.4991446 1.334350 0.8537 0.8547 0.4420626 1.337401 0.0065658 27 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 26 1000 10 linear linear 0.5670 0.4515965 0.4366829 0.0149136 0.4989892 0.5012942 1.330465 0.7786 0.8025 0.4485254 1.326873 0.0118426 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 27 1000 10 linear linear 0.5800 0.4563040 0.4367816 0.0195224 0.5004853 0.5010347 1.342513 0.7433 0.7894 0.4502645 1.337034 0.0134829 34 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 28 1000 10 linear linear 0.5255 0.4622749 0.4369494 0.0253255 0.5001363 0.5018058 1.326384 0.6195 0.8699 0.4427291 1.319849 0.0057797 31 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 29 1000 10 linear linear 0.3960 0.4482016 0.4372887 0.0109129 0.5005950 0.5018803 1.323066 0.8113 0.8197 0.4472915 1.324166 0.0100028 32 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 30 1000 10 linear linear 0.5860 0.4478118 0.4351865 0.0126253 0.4996525 0.4991935 1.322753 0.7945 0.8241 0.4447383 1.315391 0.0095518 32 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 31 1000 10 linear linear 0.4760 0.4588448 0.4368324 0.0220123 0.4996780 0.5003566 1.356358 0.7296 0.7967 0.4498861 1.357842 0.0130536 31 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 32 1000 10 linear linear 0.5390 0.4430097 0.4357999 0.0072098 0.4997591 0.4992368 1.326380 0.8550 0.8476 0.4428387 1.338633 0.0070388 29 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 33 1000 10 linear linear 0.4425 0.4598323 0.4372439 0.0225884 0.5020999 0.5009813 1.344723 0.7164 0.8311 0.4463771 1.344143 0.0091332 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 34 1000 10 linear linear 0.5810 0.4686766 0.4366017 0.0320749 0.5006519 0.5006231 1.334085 0.7323 0.8401 0.4446012 1.328794 0.0079995 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 35 1000 10 linear linear 0.5470 0.4536711 0.4355003 0.0181708 0.5001451 0.4989731 1.331757 0.7457 0.8193 0.4454383 1.320487 0.0099380 31 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 36 1000 10 linear linear 0.4710 0.4494744 0.4333920 0.0160824 0.4988085 0.4969403 1.330891 0.7707 0.8205 0.4431959 1.326726 0.0098039 30 0.5 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 37 1000 10 linear linear 0.3580 0.4671142 0.4353880 0.0317262 0.4995442 0.4979848 1.331155 0.6509 0.7874 0.4491958 1.335862 0.0138078 30 0.2 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 38 1000 10 linear linear 0.5455 0.4468642 0.4351396 0.0117246 0.4974295 0.5010358 1.338870 0.8099 0.8370 0.4439057 1.348110 0.0087661 33 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 39 1000 10 linear linear 0.5205 0.4513669 0.4366955 0.0146713 0.4991360 0.5024089 1.329331 0.7810 0.8007 0.4487387 1.332666 0.0120432 39 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 40 1000 10 linear linear 0.4115 0.4550314 0.4357550 0.0192763 0.5002786 0.4998745 1.343689 0.7527 0.7696 0.4523587 1.347792 0.0166037 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 41 1000 10 linear linear 0.4935 0.4535867 0.4373142 0.0162725 0.5003485 0.5005695 1.326083 0.7646 0.8132 0.4480239 1.324016 0.0107096 33 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 42 1000 10 linear linear 0.5665 0.4714548 0.4341484 0.0373065 0.4992996 0.4970228 1.340792 0.6461 0.7844 0.4490886 1.333696 0.0149402 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 43 1000 10 linear linear 0.4640 0.4581231 0.4362460 0.0218771 0.4997130 0.4998055 1.336351 0.7268 0.7602 0.4535738 1.336858 0.0173278 35 0.3 0.7 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 44 1000 10 linear linear 0.4960 0.4602008 0.4378585 0.0223423 0.4998582 0.5032614 1.350396 0.7282 0.7847 0.4517219 1.336767 0.0138634 39 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 45 1000 10 linear linear 0.4455 0.4499525 0.4357051 0.0142474 0.4989978 0.4992330 1.352595 0.7881 0.8687 0.4412294 1.334948 0.0055243 34 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 46 1000 10 linear linear 0.4440 0.4523269 0.4351494 0.0171775 0.4988354 0.5001585 1.342045 0.7568 0.8079 0.4465603 1.344103 0.0114109 33 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 47 1000 10 linear linear 0.3020 0.4668106 0.4365370 0.0302736 0.4993247 0.5009499 1.348826 0.6820 0.7785 0.4511930 1.355388 0.0146560 37 0.2 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 48 1000 10 linear linear 0.5320 0.4454396 0.4365026 0.0089370 0.5006808 0.4984887 1.342790 0.8272 0.8452 0.4439118 1.347677 0.0074092 30 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 49 1000 10 linear linear 0.5675 0.4456670 0.4360149 0.0096521 0.4997332 0.4993701 1.338437 0.8285 0.8360 0.4446484 1.340045 0.0086335 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 50 1000 10 linear linear 0.6050 0.4535636 0.4348902 0.0186733 0.5001091 0.4982713 1.345980 0.7536 0.7960 0.4477623 1.341553 0.0128721 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 51 1000 10 linear linear 0.4870 0.4486747 0.4350762 0.0135984 0.4992280 0.4996737 1.338327 0.7903 0.8295 0.4442322 1.338689 0.0091559 31 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 52 1000 10 linear linear 0.5020 0.4475813 0.4369376 0.0106437 0.5005356 0.5024085 1.324781 0.8181 0.8829 0.4415691 1.320155 0.0046314 31 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 53 1000 10 linear linear 0.4235 0.4456616 0.4375812 0.0080804 0.5008836 0.5015723 1.341566 0.8340 0.8300 0.4459605 1.346441 0.0083794 36 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 54 1000 10 linear linear 0.4765 0.4526539 0.4367386 0.0159152 0.5002536 0.5006561 1.341389 0.7711 0.7864 0.4505185 1.344756 0.0137799 28 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 55 1000 10 linear linear 0.4750 0.4548868 0.4351003 0.0197865 0.4987271 0.4994266 1.329500 0.7212 0.9014 0.4380473 1.324067 0.0029470 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 56 1000 10 linear linear 0.5325 0.4531644 0.4363451 0.0168193 0.5002188 0.4997813 1.339052 0.7628 0.8495 0.4433197 1.349144 0.0069746 38 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 57 1000 10 linear linear 0.5990 0.4458862 0.4357850 0.0101012 0.5009905 0.4995283 1.332253 0.8191 0.8458 0.4429851 1.330620 0.0072001 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 58 1000 10 linear linear 0.5865 0.4475562 0.4365757 0.0109805 0.4996077 0.5005528 1.335051 0.8133 0.8274 0.4462666 1.336418 0.0096909 34 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 59 1000 10 linear linear 0.5310 0.4538992 0.4364750 0.0174243 0.5009785 0.4990427 1.336718 0.7574 0.8038 0.4487438 1.333182 0.0122688 28 0.6 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 60 1000 10 linear linear 0.4405 0.4538140 0.4374263 0.0163877 0.4997964 0.5017643 1.338469 0.7689 0.8620 0.4434824 1.326314 0.0060561 32 0.3 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 61 1000 10 linear linear 0.6070 0.4500077 0.4343350 0.0156727 0.4990282 0.4974549 1.353339 0.7690 0.7936 0.4476152 1.355278 0.0132802 29 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 62 1000 10 linear linear 0.4680 0.4526380 0.4389227 0.0137153 0.5015610 0.5027825 1.325409 0.7892 0.8428 0.4466853 1.318451 0.0077626 32 0.7 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 63 1000 10 linear linear 0.5275 0.4624843 0.4349081 0.0275762 0.4991977 0.4982622 1.340683 0.7140 0.8433 0.4427070 1.332890 0.0077989 42 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 64 1000 10 linear linear 0.5950 0.4458863 0.4364945 0.0093919 0.5004936 0.5002172 1.329670 0.8280 0.8732 0.4416668 1.329490 0.0051724 29 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 65 1000 10 linear linear 0.4795 0.4605950 0.4380455 0.0225495 0.5026375 0.5015685 1.323570 0.7091 0.8800 0.4428875 1.323495 0.0048420 35 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 66 1000 10 linear linear 0.5665 0.4402349 0.4333299 0.0069051 0.4989783 0.4966731 1.329868 0.8491 0.8630 0.4390905 1.327302 0.0057606 35 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 67 1000 10 linear linear 0.5240 0.4653347 0.4352748 0.0300599 0.4982016 0.4990380 1.340268 0.6682 0.7974 0.4474343 1.343462 0.0121596 36 0.3 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 68 1000 10 linear linear 0.6005 0.4574857 0.4390315 0.0184542 0.5018307 0.5035352 1.330276 0.7465 0.8096 0.4498765 1.324727 0.0108450 38 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 69 1000 10 linear linear 0.2855 0.4559201 0.4363040 0.0196160 0.5002216 0.5005721 1.344043 0.7482 0.8561 0.4429275 1.323774 0.0066235 36 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 70 1000 10 linear linear 0.4925 0.4524706 0.4345548 0.0179158 0.4992660 0.4989538 1.334707 0.7554 0.8284 0.4438072 1.330849 0.0092524 36 0.1 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 71 1000 10 linear linear 0.1580 0.4647543 0.4365776 0.0281766 0.4995024 0.5018291 1.340213 0.6672 0.8937 0.4397747 1.336335 0.0031971 33 0.4 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 72 1000 10 linear linear 0.5800 0.4513562 0.4340337 0.0173225 0.4986338 0.4984062 1.336986 0.7641 0.7743 0.4501962 1.336875 0.0161625 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 73 1000 10 linear linear 0.4435 0.4772563 0.4361661 0.0410902 0.5003406 0.5001309 1.343939 0.6033 0.7868 0.4495696 1.334314 0.0134035 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 74 1000 10 linear linear 0.2360 0.4735757 0.4374564 0.0361193 0.5025579 0.4999358 1.350378 0.6411 0.7308 0.4597430 1.349086 0.0222866 36 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 75 1000 10 linear linear 0.5010 0.4524681 0.4333219 0.0191462 0.4976081 0.4975996 1.317101 0.7295 0.8626 0.4395487 1.315125 0.0062268 35 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 76 1000 10 linear linear 0.5560 0.4497655 0.4384315 0.0113340 0.5007598 0.5035217 1.354819 0.8120 0.8406 0.4465508 1.352931 0.0081194 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 77 1000 10 linear linear 0.5580 0.4417683 0.4373921 0.0043762 0.5007282 0.5018113 1.321566 0.8846 0.8865 0.4415146 1.322697 0.0041225 33 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 78 1000 10 linear linear 0.5455 0.4492256 0.4348895 0.0143361 0.4995924 0.4982915 1.330234 0.7791 0.8477 0.4419468 1.337346 0.0070573 28 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 79 1000 10 linear linear 0.4905 0.5049607 0.4358631 0.0690976 0.4990510 0.4997963 1.349702 0.4568 0.7660 0.4518946 1.338111 0.0160315 33 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 80 1000 10 linear linear 0.5640 0.4799154 0.4344682 0.0454472 0.4981686 0.4997693 1.346591 0.5916 0.7812 0.4489564 1.331674 0.0144882 30 0.5 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 81 1000 10 linear linear 0.5135 0.4521932 0.4358195 0.0163737 0.4988199 0.4999409 1.336504 0.7600 0.8461 0.4431508 1.338959 0.0073313 31 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 82 1000 10 linear linear 0.4660 0.4626864 0.4362278 0.0264587 0.5006575 0.5004495 1.334735 0.7075 0.7734 0.4526272 1.336814 0.0163994 29 0.6 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 83 1000 10 linear linear 0.5335 0.4435912 0.4387221 0.0048691 0.5008415 0.5018300 1.338942 0.8829 0.8536 0.4450471 1.349964 0.0063251 37 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 84 1000 10 linear linear 0.5530 0.4648819 0.4366048 0.0282770 0.5004396 0.4997360 1.333653 0.6707 0.8120 0.4477258 1.327541 0.0111210 41 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 85 1000 10 linear linear 0.5510 0.4458947 0.4340778 0.0118169 0.4985860 0.4984087 1.332201 0.8151 0.8550 0.4401641 1.327057 0.0060863 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 86 1000 10 linear linear 0.5300 0.4717076 0.4352762 0.0364314 0.4993599 0.5002697 1.340710 0.6305 0.8102 0.4461953 1.331377 0.0109191 37 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 87 1000 10 linear linear 0.4995 0.4463555 0.4351329 0.0112226 0.4998928 0.4979902 1.325641 0.8114 0.8296 0.4444715 1.323016 0.0093385 33 0.4 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 88 1000 10 linear linear 0.4950 0.4459507 0.4365020 0.0094487 0.5008235 0.4996991 1.341554 0.8267 0.8372 0.4448266 1.358602 0.0083246 33 0.4 0.7 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 89 1000 10 linear linear 0.4160 0.4574168 0.4349101 0.0225067 0.4993988 0.4958372 1.328137 0.7171 0.8407 0.4428247 1.323795 0.0079145 30 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 90 1000 10 linear linear 0.4190 0.4656331 0.4344809 0.0311522 0.4984306 0.4968016 1.342002 0.6663 0.7842 0.4485674 1.324762 0.0140864 37 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 91 1000 10 linear linear 0.3415 0.4829159 0.4362893 0.0466266 0.5002933 0.5005365 1.348102 0.6062 0.7175 0.4608475 1.341526 0.0245582 40 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 92 1000 10 linear linear 0.5020 0.4439125 0.4352084 0.0087041 0.5008363 0.4985683 1.327221 0.8379 0.8647 0.4407021 1.330165 0.0054937 33 0.5 0.9 1.0 0.7142857 0.8571429 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 93 1000 10 linear linear 0.5585 0.4444700 0.4351145 0.0093555 0.5002889 0.4982278 1.330152 0.8285 0.8051 0.4469288 1.328320 0.0118143 27 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 94 1000 10 linear linear 0.5505 0.4557600 0.4357987 0.0199614 0.4986243 0.5009526 1.324766 0.7627 0.8395 0.4437771 1.314763 0.0079784 36 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 95 1000 10 linear linear 0.4065 0.4721388 0.4359417 0.0361972 0.4999966 0.4996038 1.344913 0.6409 0.7622 0.4533263 1.328226 0.0173846 49 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 96 1000 10 linear linear 0.4760 0.4441387 0.4331346 0.0110041 0.4979611 0.4967950 1.336707 0.8280 0.7737 0.4486382 1.342148 0.0155036 31 0.4 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 97 1000 10 linear linear 0.5415 0.4455736 0.4374250 0.0081486 0.5016437 0.5008826 1.323951 0.8400 0.7975 0.4502586 1.328003 0.0128336 31 0.0 0.5 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 98 1000 10 linear linear 0.5880 0.4626132 0.4349809 0.0276323 0.4998557 0.5000815 1.343797 0.6973 0.7382 0.4560138 1.340303 0.0210330 36 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 99 1000 10 linear linear 0.4980 0.4439905 0.4343042 0.0096862 0.4989588 0.4981815 1.330847 0.8167 0.8713 0.4394335 1.341168 0.0051292 29 0.6 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
5 100 1000 10 linear linear 0.5110 0.4524753 0.4361892 0.0162861 0.5005410 0.4991244 1.326798 0.7878 0.7774 0.4514009 1.334926 0.0152117 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=linear
6 1 2000 10 linear linear 0.5215 0.4620945 0.4374013 0.0246932 0.5014832 0.5018240 1.321744 0.6801 0.8910 0.4412811 1.320742 0.0038797 36 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 2 2000 10 linear linear 0.4990 0.4512496 0.4375155 0.0137341 0.5013615 0.5009167 1.323980 0.7832 0.8549 0.4440885 1.319185 0.0065730 33 0.3 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 3 2000 10 linear linear 0.5005 0.4421573 0.4355322 0.0066250 0.4997350 0.4990919 1.320753 0.8481 0.9138 0.4379091 1.317202 0.0023769 43 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 4 2000 10 linear linear 0.6035 0.4432086 0.4378886 0.0053200 0.5010930 0.5005460 1.322869 0.8678 0.8538 0.4443342 1.323970 0.0064456 37 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 5 2000 10 linear linear 0.5990 0.4446629 0.4352785 0.0093845 0.4995725 0.4983900 1.323419 0.8340 0.8134 0.4461813 1.322965 0.0109028 42 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 6 2000 10 linear linear 0.3995 0.4609179 0.4333333 0.0275846 0.4995429 0.4964191 1.324887 0.6821 0.8363 0.4421291 1.325423 0.0087958 41 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 7 2000 10 linear linear 0.4985 0.4484841 0.4356071 0.0128770 0.5005858 0.4992366 1.322916 0.7972 0.8493 0.4429915 1.318110 0.0073844 49 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 8 2000 10 linear linear 0.5260 0.4468778 0.4351184 0.0117594 0.5010768 0.4989164 1.324873 0.8063 0.8409 0.4432532 1.325452 0.0081348 37 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 9 2000 10 linear linear 0.6035 0.4463695 0.4351064 0.0112631 0.4983304 0.4988618 1.324831 0.8030 0.8858 0.4395129 1.322034 0.0044065 42 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 10 2000 10 linear linear 0.4390 0.4423417 0.4360623 0.0062794 0.4998528 0.5003575 1.337487 0.8602 0.8709 0.4414875 1.328292 0.0054252 44 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 11 2000 10 linear linear 0.5395 0.4432739 0.4369086 0.0063654 0.5005769 0.5013793 1.316756 0.8612 0.8846 0.4409805 1.315336 0.0040720 40 0.5 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 12 2000 10 linear linear 0.5505 0.4531351 0.4360579 0.0170773 0.4999909 0.5003906 1.324186 0.7654 0.8272 0.4459254 1.319060 0.0098676 46 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 13 2000 10 linear linear 0.4630 0.4382669 0.4348344 0.0034325 0.4984237 0.4993928 1.324467 0.9113 0.8679 0.4402102 1.323082 0.0053758 36 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 14 2000 10 linear linear 0.4175 0.4479028 0.4368923 0.0110105 0.5004924 0.5010589 1.329203 0.8001 0.9048 0.4400136 1.323992 0.0031213 43 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 15 2000 10 linear linear 0.5970 0.4443458 0.4363023 0.0080435 0.5016895 0.5001997 1.334754 0.8436 0.8433 0.4444530 1.334768 0.0081507 47 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 16 2000 10 linear linear 0.2560 0.4662488 0.4369447 0.0293041 0.5001875 0.4992331 1.335524 0.6681 0.8006 0.4492012 1.333348 0.0122565 42 0.5 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 17 2000 10 linear linear 0.4785 0.4609656 0.4372274 0.0237382 0.5008744 0.5003308 1.328658 0.7243 0.8042 0.4491021 1.321043 0.0118747 36 0.5 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 18 2000 10 linear linear 0.5125 0.4417077 0.4365638 0.0051439 0.4997695 0.5004993 1.324849 0.8702 0.8626 0.4422052 1.322189 0.0056414 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 19 2000 10 linear linear 0.5500 0.4547173 0.4356016 0.0191157 0.5007302 0.4979479 1.335190 0.7518 0.8038 0.4475298 1.336694 0.0119282 43 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 20 2000 10 linear linear 0.5730 0.4411851 0.4344977 0.0066875 0.5011830 0.4985582 1.330115 0.8548 0.8966 0.4379887 1.327488 0.0034910 41 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 21 2000 10 linear linear 0.5455 0.4413624 0.4361060 0.0052564 0.5007134 0.4978173 1.320776 0.8700 0.8525 0.4427713 1.326324 0.0066653 38 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 22 2000 10 linear linear 0.4570 0.4573152 0.4370200 0.0202952 0.5013858 0.4997479 1.331140 0.7313 0.8598 0.4434181 1.326095 0.0063981 35 0.3 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 23 2000 10 linear linear 0.5000 0.4405882 0.4347048 0.0058834 0.4973372 0.4999652 1.320228 0.8599 0.8649 0.4401618 1.320693 0.0054571 44 0.5 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 24 2000 10 linear linear 0.4705 0.4423011 0.4374809 0.0048202 0.5014011 0.5016922 1.316182 0.8795 0.9017 0.4405904 1.315914 0.0031095 39 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 25 2000 10 linear linear 0.5535 0.4452843 0.4341919 0.0110924 0.4990361 0.4984729 1.328035 0.8097 0.8525 0.4413286 1.325997 0.0071367 47 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 26 2000 10 linear linear 0.5460 0.4378912 0.4339025 0.0039887 0.4989872 0.4969537 1.322115 0.8888 0.9111 0.4364407 1.319496 0.0025383 40 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 27 2000 10 linear linear 0.5755 0.4445213 0.4373900 0.0071313 0.4998224 0.5021728 1.323463 0.8498 0.8618 0.4436188 1.319860 0.0062288 38 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 28 2000 10 linear linear 0.3755 0.4638839 0.4363161 0.0275679 0.4986309 0.5005832 1.333597 0.6899 0.8817 0.4407854 1.318786 0.0044694 43 0.5 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 29 2000 10 linear linear 0.5790 0.4463500 0.4362403 0.0101097 0.5004697 0.5006198 1.340433 0.8163 0.8721 0.4415626 1.340431 0.0053223 42 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 30 2000 10 linear linear 0.5955 0.4461506 0.4356804 0.0104703 0.4995714 0.4998971 1.323054 0.8060 0.8768 0.4405430 1.322363 0.0048627 42 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 31 2000 10 linear linear 0.5345 0.4412765 0.4344078 0.0068687 0.4997114 0.4974156 1.323356 0.8501 0.8672 0.4398900 1.319502 0.0054822 49 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 32 2000 10 linear linear 0.4735 0.4483390 0.4371974 0.0111415 0.5013316 0.5012693 1.322850 0.8105 0.8537 0.4442837 1.320873 0.0070863 41 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 33 2000 10 linear linear 0.5360 0.4600923 0.4353788 0.0247135 0.4995401 0.4975490 1.326709 0.6878 0.8942 0.4389685 1.322536 0.0035897 39 0.3 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 34 2000 10 linear linear 0.5295 0.4461374 0.4370624 0.0090751 0.4998123 0.5006456 1.313040 0.8303 0.8546 0.4437719 1.309381 0.0067095 36 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 35 2000 10 linear linear 0.5335 0.4432682 0.4356830 0.0075852 0.4991486 0.4988011 1.335823 0.8396 0.8613 0.4411784 1.335537 0.0054955 34 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 36 2000 10 linear linear 0.3080 0.4687528 0.4365893 0.0321635 0.5004276 0.5006913 1.337129 0.6662 0.8764 0.4416109 1.328812 0.0050216 38 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 37 2000 10 linear linear 0.5770 0.4506272 0.4346872 0.0159400 0.4990040 0.4987651 1.324984 0.7706 0.8476 0.4419831 1.321192 0.0072959 41 0.7 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 38 2000 10 linear linear 0.5935 0.4486862 0.4375159 0.0111704 0.4996318 0.5016241 1.324720 0.8107 0.8062 0.4490870 1.322136 0.0115711 40 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 39 2000 10 linear linear 0.5870 0.4576490 0.4361106 0.0215384 0.5005000 0.5000856 1.324137 0.7318 0.7850 0.4504760 1.321805 0.0143654 48 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 40 2000 10 linear linear 0.3645 0.4473781 0.4348782 0.0124998 0.4998092 0.4982534 1.320132 0.7929 0.9181 0.4369729 1.323051 0.0020947 43 0.5 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 41 2000 10 linear linear 0.5300 0.4471892 0.4342467 0.0129425 0.4988078 0.4970856 1.335133 0.7993 0.8200 0.4445924 1.337497 0.0103457 34 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 42 2000 10 linear linear 0.5465 0.4418156 0.4358859 0.0059297 0.4992860 0.4989837 1.324913 0.8629 0.8893 0.4397694 1.321423 0.0038835 36 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 43 2000 10 linear linear 0.4590 0.4456322 0.4385084 0.0071237 0.5008343 0.5028747 1.325153 0.8541 0.9052 0.4413557 1.324134 0.0028473 38 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 44 2000 10 linear linear 0.5365 0.4482608 0.4364855 0.0117753 0.4999951 0.4996112 1.324128 0.7938 0.8483 0.4435319 1.319728 0.0070465 39 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 45 2000 10 linear linear 0.5355 0.4454651 0.4360131 0.0094520 0.4996639 0.4999777 1.327192 0.8389 0.8436 0.4435415 1.329423 0.0075284 43 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 46 2000 10 linear linear 0.5565 0.4444454 0.4364359 0.0080095 0.5001377 0.4996824 1.322796 0.8417 0.8607 0.4426140 1.323821 0.0061781 32 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 47 2000 10 linear linear 0.5090 0.4425665 0.4344753 0.0080913 0.5003138 0.4968663 1.314020 0.8385 0.9001 0.4378028 1.309416 0.0033275 41 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 48 2000 10 linear linear 0.5270 0.4412756 0.4352705 0.0060051 0.4999546 0.4996122 1.324977 0.8635 0.8580 0.4417161 1.328461 0.0064456 37 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 49 2000 10 linear linear 0.5380 0.4398343 0.4365741 0.0032602 0.5015749 0.5000947 1.324139 0.8992 0.8838 0.4406879 1.329076 0.0041138 44 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 50 2000 10 linear linear 0.5480 0.4382702 0.4352546 0.0030156 0.4993484 0.4986565 1.322515 0.9025 0.9170 0.4374735 1.323851 0.0022189 38 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 51 2000 10 linear linear 0.4330 0.4409422 0.4367416 0.0042006 0.4999109 0.5001671 1.319986 0.8842 0.8983 0.4399417 1.317795 0.0032001 38 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 52 2000 10 linear linear 0.3780 0.4667145 0.4337740 0.0329405 0.4979322 0.4982884 1.332040 0.6666 0.8206 0.4443034 1.321014 0.0105294 41 0.4 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 53 2000 10 linear linear 0.5725 0.4413841 0.4375271 0.0038570 0.5011869 0.5024152 1.313989 0.8914 0.8908 0.4415008 1.313393 0.0039736 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 54 2000 10 linear linear 0.5100 0.4430419 0.4374849 0.0055570 0.5009121 0.5009554 1.317526 0.8653 0.9120 0.4399196 1.319868 0.0024347 37 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 55 2000 10 linear linear 0.5420 0.4377763 0.4342467 0.0035296 0.4984353 0.4980866 1.315197 0.8951 0.9129 0.4366288 1.316147 0.0023821 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 56 2000 10 linear linear 0.4725 0.4474369 0.4361673 0.0112696 0.4994500 0.4991401 1.331507 0.7912 0.9316 0.4376157 1.334185 0.0014484 42 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 57 2000 10 linear linear 0.5505 0.4517495 0.4372764 0.0144732 0.5011655 0.5030787 1.321736 0.7875 0.8592 0.4439656 1.324656 0.0066892 31 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 58 2000 10 linear linear 0.4680 0.4382407 0.4356568 0.0025840 0.4989364 0.5015619 1.322051 0.9057 0.9239 0.4375088 1.321515 0.0018521 36 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 59 2000 10 linear linear 0.5205 0.4621764 0.4364439 0.0257325 0.4993896 0.5002849 1.334640 0.6941 0.8360 0.4446364 1.329055 0.0081925 39 0.4 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 60 2000 10 linear linear 0.5490 0.4580688 0.4354763 0.0225925 0.4997880 0.4991496 1.318839 0.7046 0.8618 0.4414817 1.317678 0.0060054 39 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 61 2000 10 linear linear 0.5465 0.4383234 0.4357801 0.0025433 0.4987885 0.5005632 1.316343 0.9118 0.9284 0.4374197 1.314691 0.0016396 37 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 62 2000 10 linear linear 0.5375 0.4445433 0.4360655 0.0084779 0.5009535 0.4989100 1.319186 0.8391 0.8776 0.4409882 1.316992 0.0049227 34 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 63 2000 10 linear linear 0.5065 0.4473900 0.4364337 0.0109563 0.4989408 0.5023203 1.321062 0.8152 0.8516 0.4434697 1.318388 0.0070360 40 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 64 2000 10 linear linear 0.5675 0.4425637 0.4363356 0.0062281 0.5003416 0.4998731 1.328396 0.8613 0.8511 0.4434064 1.331435 0.0070709 36 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 65 2000 10 linear linear 0.5605 0.4564141 0.4343914 0.0220227 0.4999778 0.4975808 1.324349 0.7128 0.8310 0.4431646 1.324192 0.0087732 38 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 66 2000 10 linear linear 0.4860 0.4595880 0.4357546 0.0238334 0.5008829 0.4986380 1.323792 0.7166 0.8806 0.4403780 1.318644 0.0046234 42 0.6 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 67 2000 10 linear linear 0.5665 0.4501869 0.4365562 0.0136307 0.5006389 0.5008488 1.324701 0.7891 0.8557 0.4431454 1.318310 0.0065892 39 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 68 2000 10 linear linear 0.6025 0.4469257 0.4362723 0.0106534 0.5001981 0.5003125 1.317583 0.8156 0.8971 0.4398529 1.312242 0.0035806 45 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 69 2000 10 linear linear 0.5915 0.4409665 0.4361435 0.0048230 0.4998134 0.5002072 1.323104 0.8736 0.8981 0.4395430 1.321496 0.0033995 46 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 70 2000 10 linear linear 0.5700 0.4454524 0.4375917 0.0078607 0.5010334 0.4998523 1.323323 0.8371 0.8821 0.4418414 1.316889 0.0042498 43 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 71 2000 10 linear linear 0.4620 0.4423642 0.4373768 0.0049873 0.5006483 0.5016750 1.333486 0.8732 0.9028 0.4405577 1.334887 0.0031809 42 0.4 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 72 2000 10 linear linear 0.6025 0.4421415 0.4364357 0.0057058 0.4994919 0.5005587 1.327769 0.8676 0.8857 0.4402999 1.320058 0.0038642 36 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 73 2000 10 linear linear 0.5710 0.4541813 0.4364405 0.0177408 0.4996289 0.4997819 1.326486 0.7508 0.8154 0.4466996 1.319570 0.0102592 46 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 74 2000 10 linear linear 0.4965 0.4421063 0.4369297 0.0051767 0.5004676 0.5019164 1.321295 0.8695 0.9057 0.4397593 1.321059 0.0028296 49 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 75 2000 10 linear linear 0.4520 0.4445361 0.4360807 0.0084554 0.4995198 0.5006813 1.314431 0.8771 0.8923 0.4399175 1.315335 0.0038368 42 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 76 2000 10 linear linear 0.5565 0.4506422 0.4373093 0.0133330 0.4989704 0.5032435 1.320548 0.7866 0.8446 0.4449918 1.315918 0.0076825 40 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 77 2000 10 linear linear 0.5565 0.4438917 0.4357140 0.0081776 0.5005708 0.4982385 1.324238 0.8301 0.9150 0.4379974 1.318949 0.0022833 45 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 78 2000 10 linear linear 0.5800 0.4563485 0.4340326 0.0223160 0.4979245 0.4993767 1.325738 0.7169 0.8173 0.4442685 1.323852 0.0102359 38 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 79 2000 10 linear linear 0.5055 0.4505474 0.4355135 0.0150339 0.5000333 0.4990302 1.329089 0.7882 0.7692 0.4526868 1.327955 0.0171733 34 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 80 2000 10 linear linear 0.4920 0.4399777 0.4361469 0.0038308 0.4997782 0.4993517 1.321675 0.8916 0.8889 0.4397830 1.318759 0.0036361 44 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 81 2000 10 linear linear 0.5450 0.4432160 0.4371380 0.0060780 0.5020011 0.4998674 1.326473 0.8617 0.8967 0.4407356 1.322515 0.0035976 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 82 2000 10 linear linear 0.5195 0.4624250 0.4347762 0.0276488 0.4984185 0.5004609 1.325490 0.6691 0.8454 0.4420128 1.318603 0.0072366 38 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 83 2000 10 linear linear 0.5785 0.4404898 0.4359965 0.0044932 0.4991446 0.4992604 1.314555 0.8821 0.8809 0.4404724 1.314297 0.0044759 45 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 84 2000 10 linear linear 0.5775 0.4418912 0.4353939 0.0064973 0.4983874 0.4998945 1.323203 0.8532 0.8869 0.4393354 1.320793 0.0039415 47 0.7 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 85 2000 10 linear linear 0.3970 0.4648982 0.4362973 0.0286009 0.4999155 0.4982363 1.332385 0.6543 0.8255 0.4457239 1.322710 0.0094266 41 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 86 2000 10 linear linear 0.5370 0.4418865 0.4368354 0.0050511 0.5008721 0.5016843 1.321121 0.8696 0.8952 0.4403073 1.321428 0.0034719 42 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 87 2000 10 linear linear 0.5075 0.4492245 0.4355324 0.0136921 0.4984377 0.5006863 1.318791 0.7919 0.8286 0.4445615 1.319361 0.0090291 38 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 88 2000 10 linear linear 0.4450 0.4503597 0.4372912 0.0130684 0.5021844 0.5003070 1.329107 0.8413 0.8695 0.4428556 1.315951 0.0055643 38 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 89 2000 10 linear linear 0.5165 0.4526591 0.4370174 0.0156417 0.5002121 0.5014813 1.322296 0.7716 0.8612 0.4433618 1.316477 0.0063444 41 0.3 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 90 2000 10 linear linear 0.3270 0.4615622 0.4354174 0.0261448 0.4999127 0.5006126 1.325856 0.6902 0.8193 0.4454515 1.325149 0.0100340 37 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 91 2000 10 linear linear 0.5170 0.4502125 0.4382657 0.0119468 0.5021661 0.5022444 1.320442 0.8028 0.8493 0.4453876 1.312896 0.0071220 35 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 92 2000 10 linear linear 0.4950 0.4471762 0.4358408 0.0113354 0.4995500 0.4998450 1.327965 0.7962 0.8412 0.4434207 1.326974 0.0075799 50 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 93 2000 10 linear linear 0.5875 0.4426615 0.4356221 0.0070394 0.4998800 0.4984750 1.325416 0.8401 0.9003 0.4386438 1.325958 0.0030217 38 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 94 2000 10 linear linear 0.5525 0.4445683 0.4368697 0.0076986 0.4993694 0.5016553 1.319940 0.8387 0.8576 0.4428199 1.318609 0.0059502 39 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 95 2000 10 linear linear 0.5300 0.4436015 0.4356767 0.0079248 0.4999223 0.4998145 1.327569 0.8422 0.9139 0.4381788 1.323217 0.0025021 42 0.5 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 96 2000 10 linear linear 0.5925 0.4390158 0.4366796 0.0023362 0.5008751 0.5005588 1.323536 0.9105 0.9057 0.4395619 1.325440 0.0028824 37 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 97 2000 10 linear linear 0.4910 0.4519553 0.4366948 0.0152604 0.4997041 0.5013805 1.327379 0.7736 0.8399 0.4443877 1.322498 0.0076928 40 0.5 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 98 2000 10 linear linear 0.5035 0.4521471 0.4355359 0.0166112 0.4996939 0.5013275 1.328450 0.8220 0.8586 0.4420380 1.319501 0.0065021 37 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 99 2000 10 linear linear 0.4865 0.4481562 0.4374584 0.0106979 0.5020118 0.4998563 1.320250 0.8500 0.8792 0.4418898 1.314949 0.0044314 37 0.8 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
6 100 2000 10 linear linear 0.5605 0.4610123 0.4385332 0.0224790 0.5008703 0.5039702 1.325885 0.7143 0.8716 0.4434475 1.318553 0.0049143 37 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=linear
7 1 500 5 nonlinear linear 0.6670 0.4417453 0.4209737 0.0207716 0.5244037 0.4727238 1.324759 0.7908 0.5805 0.4789127 1.339153 0.0579391 25 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 2 500 5 nonlinear linear 0.7455 0.4752435 0.4223562 0.0528873 0.5258004 0.4743991 1.343691 0.6015 0.5872 0.4782091 1.359938 0.0558529 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 3 500 5 nonlinear linear 0.6865 0.4544628 0.4232239 0.0312389 0.5246164 0.4757588 1.345221 0.7229 0.6355 0.4712238 1.354148 0.0479999 23 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 4 500 5 nonlinear linear 0.7420 0.4613488 0.4250632 0.0362856 0.5267059 0.4778599 1.332038 0.7195 0.5976 0.4785341 1.342941 0.0534709 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 5 500 5 nonlinear linear 0.6360 0.4608354 0.4240123 0.0368231 0.5260485 0.4764092 1.366385 0.6966 0.5648 0.4810582 1.376770 0.0570459 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 6 500 5 nonlinear linear 0.6770 0.4485615 0.4242352 0.0243263 0.5262382 0.4771486 1.326157 0.7663 0.6493 0.4727961 1.348408 0.0485609 27 0.2 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 7 500 5 nonlinear linear 0.6770 0.4758032 0.4240394 0.0517638 0.5238680 0.4777282 1.339891 0.5992 0.6601 0.4678976 1.360356 0.0438582 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 8 500 5 nonlinear linear 0.6665 0.4455342 0.4229338 0.0226005 0.5251154 0.4763886 1.321210 0.7867 0.5693 0.4818157 1.347852 0.0588820 24 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 9 500 5 nonlinear linear 0.6900 0.4449854 0.4224661 0.0225192 0.5256963 0.4751410 1.334592 0.8136 0.5864 0.4785730 1.359996 0.0561069 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 10 500 5 nonlinear linear 0.6975 0.4871409 0.4244377 0.0627031 0.5253479 0.4770755 1.358670 0.5533 0.5339 0.4929260 1.369535 0.0684882 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 11 500 5 nonlinear linear 0.6490 0.4641480 0.4210268 0.0431212 0.5243898 0.4737372 1.339129 0.6358 0.5721 0.4747620 1.354337 0.0537352 29 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 12 500 5 nonlinear linear 0.5730 0.4694037 0.4226694 0.0467342 0.5252546 0.4747440 1.358772 0.6419 0.6046 0.4751584 1.358419 0.0524889 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 13 500 5 nonlinear linear 0.6585 0.4903710 0.4256522 0.0647187 0.5272835 0.4792422 1.346664 0.5904 0.5878 0.4827427 1.348848 0.0570904 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 14 500 5 nonlinear linear 0.6890 0.4704490 0.4217525 0.0486966 0.5237426 0.4741157 1.350917 0.5982 0.5522 0.4796588 1.346963 0.0579063 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 15 500 5 nonlinear linear 0.5900 0.4431418 0.4231819 0.0199599 0.5258711 0.4750521 1.321856 0.7917 0.5815 0.4811324 1.365600 0.0579505 23 0.0 0.6 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 16 500 5 nonlinear linear 0.7360 0.4812198 0.4214066 0.0598132 0.5233854 0.4750910 1.343465 0.5993 0.5702 0.4824126 1.375843 0.0610060 22 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 17 500 5 nonlinear linear 0.6230 0.4406667 0.4209434 0.0197234 0.5240949 0.4738777 1.319739 0.8057 0.6117 0.4749768 1.335780 0.0540335 28 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 18 500 5 nonlinear linear 0.7705 0.4490651 0.4262240 0.0228411 0.5279628 0.4788660 1.326303 0.7786 0.5450 0.4925551 1.354115 0.0663311 20 0.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 19 500 5 nonlinear linear 0.6255 0.4898358 0.4199046 0.0699312 0.5246721 0.4711054 1.396108 0.5444 0.5674 0.4810648 1.364456 0.0611603 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 20 500 5 nonlinear linear 0.6850 0.4546790 0.4238358 0.0308431 0.5254157 0.4772056 1.322077 0.7498 0.7566 0.4534104 1.348102 0.0295745 22 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 21 500 5 nonlinear linear 0.7535 0.4947070 0.4237708 0.0709362 0.5252106 0.4766084 1.339381 0.5808 0.5298 0.4958700 1.348045 0.0720992 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 22 500 5 nonlinear linear 0.6545 0.4735043 0.4247007 0.0488036 0.5271840 0.4774460 1.351124 0.6411 0.6594 0.4693278 1.364708 0.0446271 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 23 500 5 nonlinear linear 0.6420 0.4870232 0.4233165 0.0637067 0.5243172 0.4764203 1.363516 0.5566 0.5631 0.4855435 1.358860 0.0622270 23 0.6 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 24 500 5 nonlinear linear 0.7175 0.4774891 0.4237715 0.0537176 0.5251331 0.4768690 1.350191 0.5955 0.6059 0.4765743 1.353605 0.0528028 20 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 25 500 5 nonlinear linear 0.6870 0.4439500 0.4234724 0.0204776 0.5255392 0.4768052 1.336329 0.7985 0.6623 0.4672692 1.343991 0.0437968 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 26 500 5 nonlinear linear 0.7180 0.4536488 0.4204956 0.0331532 0.5225726 0.4737615 1.325356 0.7095 0.6092 0.4729196 1.342750 0.0524240 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 27 500 5 nonlinear linear 0.6745 0.4819403 0.4233789 0.0585614 0.5246416 0.4765316 1.349957 0.5756 0.5940 0.4802035 1.355927 0.0568246 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 28 500 5 nonlinear linear 0.6880 0.4532402 0.4238741 0.0293661 0.5243486 0.4774714 1.325277 0.7548 0.5994 0.4759874 1.346783 0.0521133 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 29 500 5 nonlinear linear 0.7340 0.4466162 0.4243087 0.0223075 0.5262214 0.4769876 1.330015 0.7948 0.5726 0.4868644 1.353798 0.0625557 20 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 30 500 5 nonlinear linear 0.7205 0.4529140 0.4239435 0.0289706 0.5256568 0.4765408 1.340259 0.7367 0.6355 0.4718121 1.355956 0.0478686 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 31 500 5 nonlinear linear 0.6750 0.4544932 0.4214562 0.0330370 0.5255018 0.4731929 1.321111 0.7067 0.5667 0.4778437 1.336261 0.0563875 26 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 32 500 5 nonlinear linear 0.7360 0.4601440 0.4230337 0.0371103 0.5246745 0.4758034 1.347012 0.6936 0.6395 0.4738502 1.367880 0.0508164 24 0.8 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 33 500 5 nonlinear linear 0.6790 0.4597047 0.4205377 0.0391670 0.5231174 0.4737369 1.330849 0.7244 0.6440 0.4679782 1.347853 0.0474405 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 34 500 5 nonlinear linear 0.6540 0.4568511 0.4239845 0.0328666 0.5256702 0.4764452 1.321501 0.7490 0.6249 0.4731640 1.341896 0.0491795 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 35 500 5 nonlinear linear 0.7145 0.4585767 0.4198842 0.0386926 0.5240512 0.4720864 1.333313 0.6957 0.5823 0.4756371 1.346478 0.0557530 27 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 36 500 5 nonlinear linear 0.2375 0.4928937 0.4247144 0.0681793 0.5264541 0.4766777 1.366301 0.5263 0.5667 0.4796101 1.349055 0.0548957 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 37 500 5 nonlinear linear 0.7440 0.4473509 0.4219731 0.0253778 0.5254570 0.4735440 1.320064 0.7902 0.5960 0.4749851 1.338405 0.0530120 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 38 500 5 nonlinear linear 0.6655 0.4376693 0.4223069 0.0153624 0.5225139 0.4758311 1.344217 0.8260 0.5636 0.4807003 1.345547 0.0583935 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 39 500 5 nonlinear linear 0.6945 0.4581613 0.4238760 0.0342854 0.5250546 0.4774186 1.360459 0.7005 0.5564 0.4850403 1.390223 0.0611643 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 40 500 5 nonlinear linear 0.5795 0.4711392 0.4215760 0.0495633 0.5252365 0.4729981 1.337341 0.6264 0.5749 0.4781905 1.347001 0.0566146 21 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 41 500 5 nonlinear linear 0.7295 0.4667881 0.4231845 0.0436036 0.5270667 0.4757647 1.347494 0.6644 0.5831 0.4816857 1.359777 0.0585013 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 42 500 5 nonlinear linear 0.5980 0.4721982 0.4226413 0.0495569 0.5233862 0.4760133 1.358211 0.6049 0.7474 0.4528746 1.354195 0.0302333 26 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 43 500 5 nonlinear linear 0.6585 0.5003380 0.4235867 0.0767512 0.5259163 0.4760806 1.357321 0.4923 0.6475 0.4698449 1.346639 0.0462581 21 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 44 500 5 nonlinear linear 0.7050 0.4600456 0.4226158 0.0374298 0.5265540 0.4748761 1.329992 0.7181 0.6499 0.4696544 1.352141 0.0470387 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 45 500 5 nonlinear linear 0.5800 0.4910457 0.4228413 0.0682044 0.5238786 0.4757171 1.377317 0.5490 0.5327 0.4906659 1.378723 0.0678247 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 46 500 5 nonlinear linear 0.5900 0.4491063 0.4252284 0.0238779 0.5250479 0.4793827 1.336312 0.7803 0.5821 0.4851763 1.377592 0.0599479 22 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 47 500 5 nonlinear linear 0.6220 0.4593937 0.4216191 0.0377746 0.5237028 0.4742189 1.334348 0.6930 0.5973 0.4745244 1.343260 0.0529053 27 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 48 500 5 nonlinear linear 0.5220 0.4930069 0.4237918 0.0692150 0.5258673 0.4753377 1.390481 0.5661 0.4299 0.5236572 1.383120 0.0998654 20 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 49 500 5 nonlinear linear 0.7275 0.4642264 0.4241034 0.0401230 0.5256903 0.4770637 1.327468 0.6766 0.5469 0.4926636 1.355360 0.0685602 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 50 500 5 nonlinear linear 0.6155 0.4774290 0.4240872 0.0533418 0.5263199 0.4763008 1.358721 0.5701 0.5657 0.4813446 1.360275 0.0572574 26 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 51 500 5 nonlinear linear 0.5910 0.4427879 0.4227928 0.0199951 0.5254554 0.4757182 1.335676 0.7932 0.6370 0.4699528 1.347566 0.0471600 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 52 500 5 nonlinear linear 0.6220 0.4697401 0.4234404 0.0462997 0.5256077 0.4767128 1.346180 0.6559 0.5995 0.4772799 1.350914 0.0538395 25 0.4 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 53 500 5 nonlinear linear 0.7290 0.4527870 0.4225908 0.0301962 0.5249859 0.4752054 1.323745 0.7743 0.5585 0.4821850 1.347694 0.0595942 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 54 500 5 nonlinear linear 0.6405 0.4435683 0.4231071 0.0204612 0.5251305 0.4758412 1.315084 0.8376 0.6150 0.4746487 1.346084 0.0515416 21 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 55 500 5 nonlinear linear 0.6645 0.4533999 0.4248889 0.0285110 0.5258544 0.4791191 1.351979 0.7709 0.7437 0.4576374 1.364213 0.0327485 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 56 500 5 nonlinear linear 0.6825 0.4559912 0.4231670 0.0328243 0.5267666 0.4761207 1.327554 0.7180 0.5406 0.4881058 1.350401 0.0649388 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 57 500 5 nonlinear linear 0.7145 0.4909369 0.4244188 0.0665182 0.5266489 0.4772927 1.346895 0.5718 0.5450 0.4922153 1.355559 0.0677965 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 58 500 5 nonlinear linear 0.5325 0.4694172 0.4219331 0.0474842 0.5253946 0.4744502 1.352576 0.6242 0.6080 0.4781021 1.359088 0.0561691 24 0.6 0.6 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 59 500 5 nonlinear linear 0.6995 0.4680263 0.4243037 0.0437226 0.5247796 0.4778380 1.359063 0.6569 0.5777 0.4806714 1.343868 0.0563677 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 60 500 5 nonlinear linear 0.7340 0.4906114 0.4236050 0.0670064 0.5246167 0.4762838 1.344306 0.5511 0.5593 0.4860160 1.344960 0.0624109 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 61 500 5 nonlinear linear 0.6415 0.4759927 0.4217317 0.0542609 0.5246899 0.4735462 1.362006 0.5943 0.6049 0.4747301 1.380343 0.0529984 23 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 62 500 5 nonlinear linear 0.7075 0.4321073 0.4223394 0.0097678 0.5247161 0.4749540 1.343933 0.8533 0.6042 0.4773974 1.354381 0.0550579 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 63 500 5 nonlinear linear 0.6645 0.4493089 0.4234042 0.0259047 0.5263483 0.4754273 1.331239 0.7705 0.5539 0.4836759 1.341578 0.0602717 27 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 64 500 5 nonlinear linear 0.7175 0.4499012 0.4216370 0.0282642 0.5257180 0.4734775 1.336273 0.7368 0.5791 0.4779555 1.354433 0.0563185 21 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 65 500 5 nonlinear linear 0.6040 0.4521174 0.4226143 0.0295031 0.5266896 0.4746115 1.331764 0.7372 0.6411 0.4711909 1.346301 0.0485766 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 66 500 5 nonlinear linear 0.5655 0.4549989 0.4232830 0.0317158 0.5264383 0.4757167 1.340452 0.7204 0.5875 0.4795004 1.365202 0.0562174 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 67 500 5 nonlinear linear 0.6790 0.4469329 0.4224373 0.0244956 0.5243010 0.4754357 1.337824 0.7722 0.5670 0.4802428 1.351068 0.0578055 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 68 500 5 nonlinear linear 0.6375 0.4814934 0.4232940 0.0581994 0.5255665 0.4759022 1.356131 0.6056 0.5535 0.4888320 1.358824 0.0655379 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 69 500 5 nonlinear linear 0.7170 0.4834734 0.4234705 0.0600030 0.5272609 0.4755174 1.347728 0.5846 0.5851 0.4790845 1.351211 0.0556140 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 70 500 5 nonlinear linear 0.6895 0.4458953 0.4212143 0.0246809 0.5215315 0.4747675 1.325964 0.7797 0.5769 0.4775958 1.339769 0.0563815 23 0.4 0.6 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 71 500 5 nonlinear linear 0.6545 0.4862885 0.4232817 0.0630069 0.5265174 0.4756021 1.357617 0.5525 0.4430 0.5158183 1.361303 0.0925366 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 72 500 5 nonlinear linear 0.7110 0.4476698 0.4246304 0.0230393 0.5258423 0.4772977 1.325681 0.7735 0.5424 0.4899030 1.347353 0.0652725 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 73 500 5 nonlinear linear 0.6920 0.4373640 0.4231470 0.0142171 0.5266450 0.4749780 1.341094 0.8328 0.5184 0.4977058 1.359317 0.0745588 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 74 500 5 nonlinear linear 0.6515 0.4738759 0.4221762 0.0516998 0.5274270 0.4731844 1.326872 0.6385 0.5801 0.4819899 1.341962 0.0598137 25 0.2 0.4 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 75 500 5 nonlinear linear 0.7230 0.4301744 0.4212442 0.0089303 0.5252213 0.4726789 1.318489 0.8810 0.6309 0.4699484 1.345483 0.0487043 19 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 76 500 5 nonlinear linear 0.5735 0.4307721 0.4228261 0.0079460 0.5261552 0.4743360 1.335047 0.8805 0.5652 0.4792755 1.346483 0.0564494 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 77 500 5 nonlinear linear 0.6400 0.4460208 0.4219186 0.0241022 0.5237036 0.4747384 1.332085 0.7713 0.6126 0.4729233 1.350775 0.0510047 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 78 500 5 nonlinear linear 0.7185 0.4565481 0.4222259 0.0343222 0.5240892 0.4750988 1.348424 0.7235 0.6302 0.4713280 1.364194 0.0491021 21 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 79 500 5 nonlinear linear 0.5220 0.4816578 0.4241142 0.0575435 0.5256212 0.4776378 1.361121 0.5731 0.6102 0.4766273 1.363371 0.0525130 17 0.4 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 80 500 5 nonlinear linear 0.4895 0.4431866 0.4248895 0.0182971 0.5261905 0.4780118 1.327473 0.8073 0.5555 0.4848140 1.359510 0.0599245 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 81 500 5 nonlinear linear 0.6685 0.4688765 0.4222074 0.0466691 0.5264260 0.4737374 1.335536 0.6590 0.5010 0.4986710 1.352539 0.0764636 21 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 82 500 5 nonlinear linear 0.6240 0.4439591 0.4246218 0.0193374 0.5280423 0.4763256 1.353919 0.8028 0.5544 0.4848534 1.351803 0.0602316 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 83 500 5 nonlinear linear 0.6680 0.4641700 0.4224817 0.0416883 0.5253207 0.4747221 1.328110 0.6620 0.5341 0.4850925 1.349920 0.0626108 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 84 500 5 nonlinear linear 0.6060 0.4717972 0.4229326 0.0488646 0.5262914 0.4749496 1.338607 0.6466 0.5555 0.4882959 1.363295 0.0653633 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 85 500 5 nonlinear linear 0.6615 0.4408674 0.4234276 0.0174398 0.5250484 0.4763063 1.352125 0.8122 0.5937 0.4779455 1.359508 0.0545179 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 86 500 5 nonlinear linear 0.7115 0.4293629 0.4242305 0.0051324 0.5250678 0.4780848 1.316576 0.9040 0.6347 0.4728205 1.354198 0.0485900 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 87 500 5 nonlinear linear 0.7215 0.4661815 0.4247308 0.0414507 0.5255546 0.4783621 1.337503 0.6638 0.6684 0.4671812 1.343594 0.0424504 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 88 500 5 nonlinear linear 0.6965 0.4761245 0.4244190 0.0517055 0.5278588 0.4763035 1.329059 0.5796 0.5592 0.4805020 1.349828 0.0560831 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 89 500 5 nonlinear linear 0.6730 0.4440546 0.4225419 0.0215127 0.5253305 0.4741548 1.318281 0.7825 0.6365 0.4700761 1.333114 0.0475342 20 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 90 500 5 nonlinear linear 0.6735 0.4462048 0.4221779 0.0240269 0.5259699 0.4739928 1.322958 0.8098 0.5798 0.4794628 1.353640 0.0572848 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 91 500 5 nonlinear linear 0.7295 0.4418106 0.4220309 0.0197797 0.5250915 0.4737731 1.349171 0.8049 0.5390 0.4962894 1.365292 0.0742585 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 92 500 5 nonlinear linear 0.7095 0.4318766 0.4236838 0.0081928 0.5246794 0.4766369 1.323551 0.8833 0.5249 0.4943535 1.357661 0.0706697 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 93 500 5 nonlinear linear 0.6285 0.4401318 0.4224474 0.0176844 0.5254296 0.4748116 1.323554 0.8105 0.5984 0.4752491 1.348629 0.0528017 25 0.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 94 500 5 nonlinear linear 0.6535 0.4817023 0.4234150 0.0582873 0.5260802 0.4761763 1.346717 0.6128 0.5583 0.4896345 1.364400 0.0662195 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 95 500 5 nonlinear linear 0.6035 0.4817851 0.4239274 0.0578577 0.5255715 0.4772487 1.361939 0.5889 0.5974 0.4783542 1.376311 0.0544268 23 0.0 0.6 0.8 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 96 500 5 nonlinear linear 0.5365 0.4929051 0.4218615 0.0710436 0.5238373 0.4740415 1.359324 0.5216 0.5184 0.4900258 1.361561 0.0681642 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 97 500 5 nonlinear linear 0.6140 0.4599149 0.4233874 0.0365276 0.5259247 0.4767481 1.333098 0.7033 0.6391 0.4719637 1.355716 0.0485763 23 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 98 500 5 nonlinear linear 0.7500 0.4358547 0.4233962 0.0124586 0.5256789 0.4755490 1.335025 0.8526 0.6076 0.4772015 1.356117 0.0538054 21 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 99 500 5 nonlinear linear 0.5195 0.4855024 0.4238974 0.0616050 0.5272135 0.4758605 1.381978 0.5478 0.5625 0.4812882 1.348289 0.0573908 20 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
7 100 500 5 nonlinear linear 0.5800 0.4386831 0.4222498 0.0164333 0.5263055 0.4735858 1.355770 0.8328 0.6441 0.4710983 1.361275 0.0488485 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
8 1 1000 5 nonlinear linear 0.6875 0.4409854 0.4230003 0.0179851 0.5259814 0.4752962 1.323125 0.8087 0.5670 0.4811736 1.342125 0.0581734 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 2 1000 5 nonlinear linear 0.6105 0.4252432 0.4215635 0.0036797 0.5250044 0.4735770 1.312156 0.9187 0.5403 0.4827171 1.325532 0.0611536 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 3 1000 5 nonlinear linear 0.6950 0.4390716 0.4243630 0.0147087 0.5266188 0.4775712 1.310926 0.8333 0.6049 0.4783743 1.330774 0.0540113 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 4 1000 5 nonlinear linear 0.7250 0.4403561 0.4225232 0.0178328 0.5242646 0.4750954 1.327502 0.8218 0.6269 0.4719329 1.344113 0.0494096 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 5 1000 5 nonlinear linear 0.7005 0.4351093 0.4241515 0.0109577 0.5265313 0.4767264 1.327891 0.8565 0.6671 0.4671181 1.344818 0.0429666 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 6 1000 5 nonlinear linear 0.5150 0.4416011 0.4219214 0.0196797 0.5247833 0.4752405 1.337720 0.7958 0.6212 0.4725421 1.351120 0.0506207 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 7 1000 5 nonlinear linear 0.7235 0.4411175 0.4226137 0.0185039 0.5233619 0.4762285 1.329460 0.8040 0.7112 0.4582645 1.344527 0.0356508 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 8 1000 5 nonlinear linear 0.5355 0.4663538 0.4223058 0.0440479 0.5251716 0.4755717 1.331393 0.6793 0.6090 0.4760241 1.341924 0.0537183 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 9 1000 5 nonlinear linear 0.6850 0.4803531 0.4229607 0.0573924 0.5260339 0.4755159 1.330930 0.6667 0.5980 0.4849913 1.349072 0.0620306 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 10 1000 5 nonlinear linear 0.5510 0.4554624 0.4229853 0.0324771 0.5259747 0.4759166 1.317136 0.8284 0.5641 0.4845274 1.338483 0.0615421 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 11 1000 5 nonlinear linear 0.7215 0.4676015 0.4239808 0.0436207 0.5258665 0.4777624 1.332129 0.6445 0.5847 0.4801729 1.340057 0.0561921 28 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 12 1000 5 nonlinear linear 0.7380 0.4372739 0.4225520 0.0147218 0.5243457 0.4748513 1.324507 0.8316 0.7140 0.4581070 1.339026 0.0355549 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 13 1000 5 nonlinear linear 0.6395 0.4388217 0.4224798 0.0163419 0.5253237 0.4751399 1.327936 0.8120 0.5339 0.4856848 1.346133 0.0632049 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 14 1000 5 nonlinear linear 0.7115 0.4378335 0.4214174 0.0164160 0.5244277 0.4742580 1.331152 0.8184 0.5633 0.4773711 1.351797 0.0559537 30 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 15 1000 5 nonlinear linear 0.7120 0.4475003 0.4233357 0.0241646 0.5253217 0.4764155 1.328823 0.7707 0.5693 0.4808237 1.343481 0.0574880 29 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 16 1000 5 nonlinear linear 0.6115 0.4330953 0.4217492 0.0113461 0.5267248 0.4730814 1.313268 0.8350 0.6963 0.4603191 1.340961 0.0385699 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 17 1000 5 nonlinear linear 0.7170 0.4359884 0.4208521 0.0151363 0.5222999 0.4739080 1.311728 0.8259 0.5842 0.4755925 1.338442 0.0547405 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 18 1000 5 nonlinear linear 0.6140 0.4561605 0.4238116 0.0323489 0.5256615 0.4762653 1.325014 0.7331 0.6669 0.4670488 1.338391 0.0432372 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 19 1000 5 nonlinear linear 0.5570 0.4465627 0.4225976 0.0239651 0.5240741 0.4758107 1.340700 0.7629 0.5982 0.4753574 1.346881 0.0527598 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 20 1000 5 nonlinear linear 0.6520 0.4503883 0.4227833 0.0276051 0.5263233 0.4743559 1.315426 0.7340 0.5720 0.4842037 1.351105 0.0614204 29 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 21 1000 5 nonlinear linear 0.7095 0.4477698 0.4208261 0.0269437 0.5241513 0.4731873 1.334638 0.7539 0.6199 0.4713404 1.345327 0.0505143 23 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 22 1000 5 nonlinear linear 0.6990 0.4364137 0.4233203 0.0130935 0.5251185 0.4761127 1.327978 0.8354 0.5119 0.4894521 1.348519 0.0661318 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 23 1000 5 nonlinear linear 0.6770 0.4564428 0.4243483 0.0320945 0.5252833 0.4781444 1.342497 0.7165 0.5900 0.4806808 1.337042 0.0563325 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 24 1000 5 nonlinear linear 0.6040 0.4264951 0.4218659 0.0046292 0.5270053 0.4738326 1.320059 0.9247 0.5098 0.4887864 1.340733 0.0669205 38 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 25 1000 5 nonlinear linear 0.6995 0.4318849 0.4241203 0.0077646 0.5251951 0.4770115 1.323055 0.8764 0.5261 0.4877984 1.343730 0.0636781 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 26 1000 5 nonlinear linear 0.6680 0.4391261 0.4239411 0.0151850 0.5263482 0.4767649 1.319663 0.8204 0.5824 0.4802404 1.338288 0.0562993 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 27 1000 5 nonlinear linear 0.5910 0.4499894 0.4217347 0.0282547 0.5240082 0.4740500 1.335020 0.7404 0.5885 0.4762020 1.345375 0.0544674 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 28 1000 5 nonlinear linear 0.6670 0.4296509 0.4225118 0.0071391 0.5262025 0.4748786 1.318276 0.8891 0.5765 0.4791510 1.341432 0.0566392 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 29 1000 5 nonlinear linear 0.6430 0.4550255 0.4214664 0.0335591 0.5268155 0.4733577 1.326652 0.7203 0.6410 0.4693811 1.332777 0.0479146 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 30 1000 5 nonlinear linear 0.6890 0.4366171 0.4235213 0.0130958 0.5266424 0.4753640 1.319920 0.8277 0.5735 0.4801884 1.347102 0.0566671 31 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 31 1000 5 nonlinear linear 0.6815 0.4450230 0.4238282 0.0211948 0.5251125 0.4770281 1.326334 0.7790 0.5613 0.4813653 1.341175 0.0575371 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 32 1000 5 nonlinear linear 0.6105 0.4406662 0.4236844 0.0169818 0.5255406 0.4763694 1.315083 0.8162 0.5230 0.4879535 1.328277 0.0642691 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 33 1000 5 nonlinear linear 0.5810 0.4411888 0.4205313 0.0206575 0.5222027 0.4747898 1.329244 0.7918 0.5474 0.4818020 1.343049 0.0612708 31 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 34 1000 5 nonlinear linear 0.6765 0.4375511 0.4237525 0.0137985 0.5251874 0.4765086 1.320967 0.8307 0.6021 0.4762317 1.341868 0.0524792 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 35 1000 5 nonlinear linear 0.6835 0.4431682 0.4238913 0.0192769 0.5242753 0.4774977 1.330100 0.8058 0.5624 0.4852649 1.354687 0.0613736 25 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 36 1000 5 nonlinear linear 0.7115 0.4351762 0.4249785 0.0101977 0.5274925 0.4774572 1.326146 0.8654 0.7271 0.4589574 1.343347 0.0339789 31 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 37 1000 5 nonlinear linear 0.7160 0.4298194 0.4230138 0.0068056 0.5235774 0.4773398 1.307303 0.8842 0.5212 0.4881273 1.333563 0.0651136 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 38 1000 5 nonlinear linear 0.7270 0.4288490 0.4203644 0.0084846 0.5219660 0.4736211 1.332008 0.8744 0.5663 0.4738467 1.345762 0.0534823 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 39 1000 5 nonlinear linear 0.6620 0.4305218 0.4231917 0.0073301 0.5279362 0.4744726 1.311566 0.8855 0.5693 0.4815056 1.337814 0.0583139 30 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 40 1000 5 nonlinear linear 0.6670 0.4275436 0.4241865 0.0033571 0.5256721 0.4762311 1.307813 0.9208 0.5236 0.4880388 1.333858 0.0638523 26 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 41 1000 5 nonlinear linear 0.6590 0.4511561 0.4223563 0.0287998 0.5235879 0.4746843 1.316889 0.7735 0.5707 0.4787737 1.332839 0.0564174 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 42 1000 5 nonlinear linear 0.6645 0.4552973 0.4233882 0.0319091 0.5243068 0.4759034 1.322791 0.7241 0.6463 0.4689259 1.334035 0.0455378 30 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 43 1000 5 nonlinear linear 0.7165 0.4292524 0.4248232 0.0044293 0.5274839 0.4767203 1.315770 0.9129 0.5851 0.4813043 1.335592 0.0564811 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 44 1000 5 nonlinear linear 0.6820 0.4393011 0.4246413 0.0146598 0.5241762 0.4789346 1.309546 0.8203 0.6076 0.4764242 1.330296 0.0517829 28 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 45 1000 5 nonlinear linear 0.7380 0.4421080 0.4240564 0.0180516 0.5244597 0.4778470 1.319689 0.8088 0.6435 0.4702836 1.340351 0.0462272 31 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 46 1000 5 nonlinear linear 0.7085 0.4356336 0.4235476 0.0120860 0.5247187 0.4772344 1.318402 0.8464 0.5662 0.4810872 1.344197 0.0575396 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 47 1000 5 nonlinear linear 0.7355 0.4370132 0.4223549 0.0146583 0.5247226 0.4746749 1.326342 0.8279 0.6227 0.4724645 1.346746 0.0501096 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 48 1000 5 nonlinear linear 0.7600 0.4506375 0.4233631 0.0272744 0.5242069 0.4765991 1.330875 0.7506 0.5594 0.4823482 1.340412 0.0589851 29 0.6 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 49 1000 5 nonlinear linear 0.6975 0.4391264 0.4232778 0.0158486 0.5260943 0.4752662 1.321357 0.8239 0.5835 0.4797167 1.344864 0.0564389 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 50 1000 5 nonlinear linear 0.6275 0.4340654 0.4247747 0.0092908 0.5261158 0.4780373 1.321257 0.8658 0.6086 0.4763083 1.344885 0.0515337 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 51 1000 5 nonlinear linear 0.7075 0.4443976 0.4224001 0.0219975 0.5249582 0.4744790 1.315318 0.7825 0.5718 0.4792670 1.333714 0.0568669 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 52 1000 5 nonlinear linear 0.7300 0.4472149 0.4245068 0.0227081 0.5258248 0.4778375 1.316882 0.7762 0.5828 0.4805786 1.335629 0.0560718 29 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 53 1000 5 nonlinear linear 0.6035 0.4933255 0.4247579 0.0685677 0.5266420 0.4773118 1.351354 0.5197 0.5425 0.4870653 1.344162 0.0623075 27 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 54 1000 5 nonlinear linear 0.6220 0.4294823 0.4235799 0.0059023 0.5282272 0.4756095 1.325275 0.8960 0.6651 0.4671825 1.346597 0.0436025 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 55 1000 5 nonlinear linear 0.7050 0.4375648 0.4227400 0.0148248 0.5261159 0.4748668 1.318334 0.8334 0.5796 0.4786433 1.330635 0.0559033 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 56 1000 5 nonlinear linear 0.5940 0.4466965 0.4219443 0.0247522 0.5236576 0.4753392 1.323882 0.7657 0.5466 0.4815279 1.344199 0.0595836 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 57 1000 5 nonlinear linear 0.7430 0.4269479 0.4227896 0.0041582 0.5254577 0.4756087 1.314720 0.9113 0.5451 0.4838189 1.340134 0.0610293 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 58 1000 5 nonlinear linear 0.6920 0.4941645 0.4228898 0.0712747 0.5284738 0.4735634 1.328004 0.5559 0.6390 0.4716890 1.336830 0.0487991 30 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 59 1000 5 nonlinear linear 0.7315 0.4363713 0.4242768 0.0120945 0.5268912 0.4757308 1.324507 0.8215 0.5709 0.4816438 1.348501 0.0573670 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 60 1000 5 nonlinear linear 0.6795 0.4538170 0.4226351 0.0311819 0.5248835 0.4755840 1.324617 0.7405 0.5641 0.4802899 1.334307 0.0576549 35 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 61 1000 5 nonlinear linear 0.6905 0.4371050 0.4240082 0.0130969 0.5278947 0.4755675 1.328982 0.8436 0.6526 0.4692296 1.356079 0.0452214 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 62 1000 5 nonlinear linear 0.6865 0.4504849 0.4231136 0.0273713 0.5269194 0.4750385 1.325216 0.7654 0.5530 0.4840062 1.339288 0.0608926 27 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 63 1000 5 nonlinear linear 0.7240 0.4751647 0.4236651 0.0514996 0.5270241 0.4751647 1.328685 0.4226 0.5533 0.4830105 1.331559 0.0593454 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 64 1000 5 nonlinear linear 0.7075 0.4345154 0.4226695 0.0118459 0.5253509 0.4753556 1.319708 0.8467 0.6213 0.4726748 1.341486 0.0500053 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 65 1000 5 nonlinear linear 0.6910 0.4370930 0.4230028 0.0140902 0.5245294 0.4765626 1.329134 0.8326 0.6943 0.4623193 1.339106 0.0393165 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 66 1000 5 nonlinear linear 0.6790 0.4360423 0.4225213 0.0135210 0.5262640 0.4752112 1.323551 0.8376 0.5647 0.4817369 1.343121 0.0592157 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 67 1000 5 nonlinear linear 0.7065 0.4308310 0.4242875 0.0065435 0.5252353 0.4779733 1.320089 0.8878 0.5832 0.4820412 1.348150 0.0577537 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 68 1000 5 nonlinear linear 0.6915 0.4885422 0.4232286 0.0653136 0.5257548 0.4754148 1.333034 0.5423 0.6625 0.4673816 1.345612 0.0441530 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 69 1000 5 nonlinear linear 0.6950 0.4355394 0.4220848 0.0134546 0.5274973 0.4733484 1.323501 0.8405 0.5620 0.4803619 1.343863 0.0582771 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 70 1000 5 nonlinear linear 0.6735 0.4276419 0.4220281 0.0056139 0.5245506 0.4751580 1.317948 0.8954 0.5652 0.4804054 1.344024 0.0583773 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 71 1000 5 nonlinear linear 0.5310 0.4680341 0.4228876 0.0451464 0.5260810 0.4749412 1.328847 0.6376 0.6229 0.4722690 1.346631 0.0493814 30 0.2 0.4 0.8 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 72 1000 5 nonlinear linear 0.6110 0.4254475 0.4224699 0.0029776 0.5265091 0.4746207 1.309024 0.9296 0.6262 0.4728208 1.334436 0.0503509 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 73 1000 5 nonlinear linear 0.6450 0.4371471 0.4217475 0.0153996 0.5249140 0.4733447 1.330950 0.8236 0.5790 0.4823615 1.352060 0.0606140 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 74 1000 5 nonlinear linear 0.5870 0.4678944 0.4239011 0.0439933 0.5280205 0.4756472 1.324084 0.7278 0.5792 0.4869938 1.346754 0.0630927 27 0.0 1.0 1.0 0.8571429 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 75 1000 5 nonlinear linear 0.7050 0.4548802 0.4239815 0.0308986 0.5264057 0.4761517 1.335571 0.7216 0.5612 0.4818721 1.342359 0.0578905 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 76 1000 5 nonlinear linear 0.7115 0.4539230 0.4213878 0.0325352 0.5241638 0.4738153 1.333797 0.7306 0.6888 0.4621387 1.342777 0.0407509 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 77 1000 5 nonlinear linear 0.7175 0.4758041 0.4235845 0.0522196 0.5264178 0.4761236 1.327279 0.6056 0.6485 0.4697071 1.333692 0.0461227 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 78 1000 5 nonlinear linear 0.6370 0.4418896 0.4239039 0.0179858 0.5259743 0.4766714 1.316665 0.8095 0.5810 0.4793245 1.337593 0.0554206 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 79 1000 5 nonlinear linear 0.6430 0.4515656 0.4240411 0.0275245 0.5266795 0.4768952 1.319350 0.7424 0.5490 0.4846392 1.331198 0.0605981 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 80 1000 5 nonlinear linear 0.7305 0.4312274 0.4239535 0.0072739 0.5243413 0.4769624 1.310808 0.8853 0.6805 0.4642508 1.329772 0.0402973 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 81 1000 5 nonlinear linear 0.6405 0.4432225 0.4239232 0.0192993 0.5249954 0.4783822 1.337444 0.8071 0.6114 0.4789437 1.350947 0.0550205 30 0.6 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 82 1000 5 nonlinear linear 0.6205 0.4450742 0.4222656 0.0228086 0.5268081 0.4734878 1.319448 0.7837 0.6077 0.4744334 1.344475 0.0521678 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 83 1000 5 nonlinear linear 0.6250 0.4297565 0.4219490 0.0078075 0.5247457 0.4751332 1.319672 0.8803 0.5528 0.4819365 1.338235 0.0599875 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 84 1000 5 nonlinear linear 0.7385 0.4444710 0.4235069 0.0209641 0.5249252 0.4762868 1.327637 0.7902 0.5710 0.4812425 1.346984 0.0577357 32 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 85 1000 5 nonlinear linear 0.6670 0.4401952 0.4235275 0.0166677 0.5258331 0.4761609 1.335853 0.8222 0.6155 0.4748334 1.347405 0.0513059 21 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 86 1000 5 nonlinear linear 0.6995 0.4346869 0.4246138 0.0100732 0.5279461 0.4771277 1.317702 0.8548 0.6127 0.4764266 1.345857 0.0518128 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 87 1000 5 nonlinear linear 0.6685 0.4803183 0.4228851 0.0574333 0.5243053 0.4767840 1.343781 0.5745 0.5821 0.4815055 1.347316 0.0586205 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 88 1000 5 nonlinear linear 0.7190 0.4630506 0.4241509 0.0388997 0.5260361 0.4773497 1.332560 0.7101 0.5694 0.4828744 1.343469 0.0587236 26 0.6 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 89 1000 5 nonlinear linear 0.6290 0.4410802 0.4239542 0.0171260 0.5274103 0.4770143 1.324955 0.8212 0.6420 0.4719506 1.351635 0.0479964 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 90 1000 5 nonlinear linear 0.6745 0.4411413 0.4209691 0.0201722 0.5225094 0.4736599 1.317273 0.8119 0.6488 0.4662272 1.339002 0.0452581 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 91 1000 5 nonlinear linear 0.6085 0.4390763 0.4238900 0.0151863 0.5255725 0.4767809 1.317840 0.8242 0.5752 0.4839117 1.337804 0.0600217 25 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 92 1000 5 nonlinear linear 0.7120 0.4882512 0.4208325 0.0674187 0.5232076 0.4735617 1.349381 0.5417 0.5683 0.4774478 1.348125 0.0566153 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 93 1000 5 nonlinear linear 0.7415 0.4403565 0.4226407 0.0177158 0.5240498 0.4761372 1.324194 0.8208 0.7116 0.4589961 1.338709 0.0363554 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 94 1000 5 nonlinear linear 0.6995 0.4478334 0.4224383 0.0253952 0.5254369 0.4746766 1.317596 0.7588 0.5659 0.4789698 1.339974 0.0565316 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 95 1000 5 nonlinear linear 0.6475 0.4362677 0.4229807 0.0132871 0.5242126 0.4763053 1.326186 0.8558 0.5635 0.4850046 1.342823 0.0620240 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 96 1000 5 nonlinear linear 0.6355 0.4356638 0.4249889 0.0106749 0.5270245 0.4775384 1.315856 0.8593 0.5521 0.4850711 1.331148 0.0600821 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 97 1000 5 nonlinear linear 0.6905 0.4434258 0.4262279 0.0171979 0.5278529 0.4781064 1.328143 0.8065 0.5492 0.4909038 1.348686 0.0646759 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 98 1000 5 nonlinear linear 0.6555 0.4433511 0.4231103 0.0202408 0.5252309 0.4760774 1.327082 0.7941 0.5728 0.4815529 1.348004 0.0584426 30 0.4 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 99 1000 5 nonlinear linear 0.6710 0.4328664 0.4231819 0.0096845 0.5275618 0.4737061 1.326129 0.8644 0.5788 0.4790599 1.338196 0.0558780 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
8 100 1000 5 nonlinear linear 0.6845 0.4613738 0.4224589 0.0389150 0.5254975 0.4741991 1.319207 0.6915 0.6520 0.4679211 1.335060 0.0454622 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
9 1 2000 5 nonlinear linear 0.7095 0.4309148 0.4241830 0.0067318 0.5257401 0.4769266 1.314937 0.8914 0.4802 0.4952466 1.332605 0.0710636 33 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 2 2000 5 nonlinear linear 0.7205 0.4375508 0.4235249 0.0140259 0.5245006 0.4765483 1.314873 0.8295 0.5107 0.4895812 1.332105 0.0660562 39 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 3 2000 5 nonlinear linear 0.6140 0.4447302 0.4224570 0.0222732 0.5239006 0.4748128 1.319211 0.7832 0.5991 0.4745968 1.334863 0.0521398 38 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 4 2000 5 nonlinear linear 0.6750 0.4278049 0.4225859 0.0052191 0.5249495 0.4757395 1.310787 0.9040 0.6766 0.4630704 1.332648 0.0404845 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 5 2000 5 nonlinear linear 0.7195 0.4387422 0.4228516 0.0158907 0.5236113 0.4765984 1.312890 0.8175 0.5880 0.4778704 1.329273 0.0550188 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 6 2000 5 nonlinear linear 0.7055 0.4339280 0.4234353 0.0104927 0.5265517 0.4747604 1.314119 0.8613 0.6915 0.4625523 1.337195 0.0391169 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 7 2000 5 nonlinear linear 0.7315 0.4407456 0.4232686 0.0174770 0.5258859 0.4757824 1.320749 0.8138 0.5946 0.4772502 1.337404 0.0539816 43 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 8 2000 5 nonlinear linear 0.5880 0.4324917 0.4224158 0.0100759 0.5251667 0.4738639 1.318414 0.8634 0.6605 0.4663044 1.339472 0.0438886 33 0.4 0.6 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 9 2000 5 nonlinear linear 0.7190 0.4334473 0.4236017 0.0098456 0.5261093 0.4756395 1.324392 0.8506 0.6078 0.4757643 1.347446 0.0521625 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 10 2000 5 nonlinear linear 0.6705 0.4249623 0.4234532 0.0015091 0.5259834 0.4758389 1.305690 0.9655 0.5312 0.4867987 1.329306 0.0633455 34 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 11 2000 5 nonlinear linear 0.5785 0.4357399 0.4216908 0.0140491 0.5254688 0.4735017 1.317682 0.8504 0.6510 0.4674555 1.338255 0.0457646 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 12 2000 5 nonlinear linear 0.6710 0.4330652 0.4219829 0.0110823 0.5247621 0.4739836 1.317818 0.8489 0.6299 0.4697528 1.337564 0.0477699 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 13 2000 5 nonlinear linear 0.6705 0.4281841 0.4217258 0.0064584 0.5256350 0.4740089 1.316752 0.8944 0.5047 0.4895420 1.336642 0.0678163 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 14 2000 5 nonlinear linear 0.6900 0.4270915 0.4241690 0.0029226 0.5269861 0.4765594 1.311932 0.9263 0.6079 0.4767412 1.337044 0.0525722 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 15 2000 5 nonlinear linear 0.7215 0.4288237 0.4237207 0.0051030 0.5258525 0.4763477 1.307576 0.9123 0.5467 0.4856468 1.328688 0.0619260 33 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 16 2000 5 nonlinear linear 0.6565 0.4384252 0.4244157 0.0140094 0.5271378 0.4759785 1.330417 0.8355 0.6343 0.4734642 1.349106 0.0490485 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 17 2000 5 nonlinear linear 0.6680 0.4355857 0.4247704 0.0108153 0.5270877 0.4775761 1.313598 0.8568 0.5551 0.4849104 1.326425 0.0601400 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 18 2000 5 nonlinear linear 0.7165 0.4273307 0.4216781 0.0056526 0.5246019 0.4741833 1.320174 0.8987 0.4874 0.4914066 1.337438 0.0697285 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 19 2000 5 nonlinear linear 0.6895 0.4253533 0.4234467 0.0019066 0.5271186 0.4750062 1.320785 0.9435 0.5928 0.4781407 1.338478 0.0546939 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 20 2000 5 nonlinear linear 0.6875 0.4255883 0.4218742 0.0037141 0.5235633 0.4756813 1.309002 0.9183 0.5677 0.4792261 1.338542 0.0573519 38 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 21 2000 5 nonlinear linear 0.5735 0.4447224 0.4225137 0.0222087 0.5255959 0.4746750 1.328712 0.7807 0.5363 0.4849465 1.341195 0.0624329 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 22 2000 5 nonlinear linear 0.6970 0.4327362 0.4250091 0.0077271 0.5271180 0.4768229 1.307578 0.8810 0.5471 0.4847868 1.324664 0.0597776 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 23 2000 5 nonlinear linear 0.6915 0.4392203 0.4231600 0.0160603 0.5252817 0.4758113 1.316874 0.8171 0.5686 0.4811777 1.335168 0.0580177 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 24 2000 5 nonlinear linear 0.7440 0.4330534 0.4219618 0.0110916 0.5241674 0.4747973 1.328706 0.8554 0.6431 0.4711100 1.348063 0.0491482 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 25 2000 5 nonlinear linear 0.5810 0.4302072 0.4238276 0.0063796 0.5241872 0.4772499 1.323428 0.8909 0.6734 0.4662987 1.343747 0.0424712 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 26 2000 5 nonlinear linear 0.7070 0.4249098 0.4219021 0.0030078 0.5248675 0.4743242 1.308071 0.9283 0.5683 0.4790417 1.337557 0.0571397 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 27 2000 5 nonlinear linear 0.6755 0.4317167 0.4234141 0.0083026 0.5259550 0.4760891 1.325494 0.8771 0.6737 0.4664372 1.335307 0.0430231 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 28 2000 5 nonlinear linear 0.7035 0.4340457 0.4247386 0.0093071 0.5271897 0.4773701 1.317681 0.8683 0.5219 0.4906617 1.335484 0.0659231 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 29 2000 5 nonlinear linear 0.6745 0.4327383 0.4225995 0.0101387 0.5262007 0.4755332 1.315004 0.8618 0.5964 0.4776025 1.333201 0.0550030 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 30 2000 5 nonlinear linear 0.7105 0.4343403 0.4231210 0.0112194 0.5258628 0.4751997 1.310378 0.8528 0.6055 0.4767627 1.330062 0.0536418 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 31 2000 5 nonlinear linear 0.6170 0.4353482 0.4234641 0.0118841 0.5259614 0.4759806 1.311176 0.8529 0.5222 0.4883700 1.330193 0.0649059 37 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 32 2000 5 nonlinear linear 0.7530 0.4262930 0.4215501 0.0047429 0.5253501 0.4739920 1.304956 0.9073 0.5280 0.4861745 1.331377 0.0646244 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 33 2000 5 nonlinear linear 0.6705 0.4294074 0.4228273 0.0065801 0.5239212 0.4757882 1.316047 0.8863 0.5549 0.4824303 1.340516 0.0596029 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 34 2000 5 nonlinear linear 0.6600 0.4462868 0.4234689 0.0228179 0.5263115 0.4762035 1.313484 0.7775 0.6545 0.4688921 1.334537 0.0454231 37 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 35 2000 5 nonlinear linear 0.6930 0.4337514 0.4231296 0.0106217 0.5250035 0.4771067 1.312274 0.8504 0.6298 0.4731453 1.331592 0.0500157 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 36 2000 5 nonlinear linear 0.6460 0.4283958 0.4221255 0.0062703 0.5238241 0.4755483 1.309769 0.8900 0.5691 0.4803634 1.332520 0.0582379 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 37 2000 5 nonlinear linear 0.6485 0.4303019 0.4251896 0.0051123 0.5267591 0.4785750 1.307812 0.9081 0.5646 0.4830604 1.335213 0.0578708 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 38 2000 5 nonlinear linear 0.6930 0.4233464 0.4203566 0.0029898 0.5244348 0.4724213 1.314545 0.9306 0.5827 0.4763547 1.334145 0.0559981 33 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 39 2000 5 nonlinear linear 0.7025 0.4301225 0.4206259 0.0094966 0.5219903 0.4740333 1.314102 0.8672 0.5741 0.4769169 1.333661 0.0562910 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 40 2000 5 nonlinear linear 0.7145 0.4347681 0.4247437 0.0100244 0.5266756 0.4773535 1.323132 0.8679 0.6716 0.4671262 1.341157 0.0423825 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 41 2000 5 nonlinear linear 0.7070 0.4273835 0.4221290 0.0052545 0.5244192 0.4739460 1.323186 0.9047 0.6463 0.4675277 1.340573 0.0453987 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 42 2000 5 nonlinear linear 0.7400 0.4333168 0.4241677 0.0091491 0.5267010 0.4766149 1.315284 0.8747 0.5594 0.4829167 1.334394 0.0587491 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 43 2000 5 nonlinear linear 0.7050 0.4320650 0.4239478 0.0081172 0.5258957 0.4773654 1.322114 0.8765 0.7229 0.4590210 1.340951 0.0350732 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 44 2000 5 nonlinear linear 0.7015 0.4434196 0.4240186 0.0194010 0.5278353 0.4757638 1.317422 0.7993 0.6347 0.4717303 1.326722 0.0477117 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 45 2000 5 nonlinear linear 0.7275 0.4397487 0.4201612 0.0195874 0.5222499 0.4736848 1.317732 0.8013 0.5875 0.4754183 1.341449 0.0552571 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 46 2000 5 nonlinear linear 0.6935 0.4441414 0.4230480 0.0210934 0.5256463 0.4752162 1.318299 0.7848 0.5229 0.4877474 1.328704 0.0646994 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 47 2000 5 nonlinear linear 0.6330 0.4360288 0.4263463 0.0096825 0.5256123 0.4794205 1.311027 0.8643 0.5768 0.4822389 1.333181 0.0558926 37 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 48 2000 5 nonlinear linear 0.5875 0.4311351 0.4218083 0.0093268 0.5248252 0.4733683 1.326451 0.8651 0.5634 0.4800423 1.337028 0.0582339 37 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 49 2000 5 nonlinear linear 0.7130 0.4401195 0.4234705 0.0166490 0.5265078 0.4764443 1.319608 0.8141 0.5814 0.4802947 1.334173 0.0568242 39 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 50 2000 5 nonlinear linear 0.6970 0.4363134 0.4242228 0.0120907 0.5253897 0.4776377 1.312824 0.8464 0.5803 0.4805114 1.333973 0.0562886 35 0.2 0.4 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 51 2000 5 nonlinear linear 0.6325 0.4343151 0.4245185 0.0097966 0.5264579 0.4771709 1.322141 0.8635 0.6192 0.4744393 1.340320 0.0499208 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 52 2000 5 nonlinear linear 0.5915 0.4283777 0.4238219 0.0045557 0.5261129 0.4762417 1.318643 0.9114 0.4794 0.4947296 1.336967 0.0709076 39 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 53 2000 5 nonlinear linear 0.5870 0.4512974 0.4227872 0.0285101 0.5235499 0.4762094 1.322928 0.7442 0.5478 0.4841878 1.335272 0.0614006 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 54 2000 5 nonlinear linear 0.6630 0.4376334 0.4243049 0.0133286 0.5263891 0.4768006 1.318345 0.8325 0.5732 0.4810457 1.337908 0.0567408 40 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 55 2000 5 nonlinear linear 0.7155 0.4687343 0.4244336 0.0443007 0.5265285 0.4771944 1.321557 0.6243 0.4729 0.4962753 1.337539 0.0718416 36 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 56 2000 5 nonlinear linear 0.6365 0.4292242 0.4219895 0.0072347 0.5256048 0.4739794 1.317163 0.8854 0.5995 0.4757733 1.335428 0.0537838 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 57 2000 5 nonlinear linear 0.6585 0.4270276 0.4219575 0.0050701 0.5241584 0.4750076 1.302123 0.9055 0.5627 0.4802011 1.333224 0.0582436 31 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 58 2000 5 nonlinear linear 0.6950 0.4324269 0.4240740 0.0083529 0.5267300 0.4766406 1.308993 0.8764 0.7007 0.4621230 1.329883 0.0380490 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 59 2000 5 nonlinear linear 0.6835 0.4333184 0.4221848 0.0111336 0.5238576 0.4767915 1.317469 0.8591 0.5633 0.4815930 1.339165 0.0594082 30 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 60 2000 5 nonlinear linear 0.6125 0.4279436 0.4231256 0.0048180 0.5265958 0.4750453 1.315168 0.9087 0.5252 0.4872681 1.331262 0.0641425 37 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 61 2000 5 nonlinear linear 0.7245 0.4426391 0.4244874 0.0181517 0.5258249 0.4771401 1.313692 0.8180 0.6783 0.4651031 1.332557 0.0406157 38 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 62 2000 5 nonlinear linear 0.6325 0.4433874 0.4226193 0.0207680 0.5231986 0.4762617 1.321347 0.7974 0.5174 0.4867190 1.332016 0.0640997 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 63 2000 5 nonlinear linear 0.6140 0.4295301 0.4229670 0.0065631 0.5250160 0.4751200 1.305484 0.8858 0.5577 0.4817828 1.330591 0.0588158 40 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 64 2000 5 nonlinear linear 0.6665 0.4330746 0.4232860 0.0097886 0.5251227 0.4766768 1.308735 0.8677 0.6640 0.4671737 1.329659 0.0438877 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 65 2000 5 nonlinear linear 0.6285 0.4280958 0.4207205 0.0073753 0.5235499 0.4734779 1.319902 0.8861 0.6342 0.4686324 1.337872 0.0479120 38 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 66 2000 5 nonlinear linear 0.6525 0.4526050 0.4222528 0.0303522 0.5247832 0.4752146 1.325033 0.7568 0.6019 0.4752596 1.337161 0.0530068 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 67 2000 5 nonlinear linear 0.6285 0.4247109 0.4225261 0.0021848 0.5240044 0.4761166 1.310179 0.9379 0.5144 0.4882298 1.333002 0.0657037 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 68 2000 5 nonlinear linear 0.6430 0.4238458 0.4224679 0.0013779 0.5259563 0.4743998 1.323321 0.9525 0.5231 0.4875403 1.340688 0.0650724 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 69 2000 5 nonlinear linear 0.6545 0.4311377 0.4226931 0.0084446 0.5235570 0.4764980 1.307783 0.8745 0.5319 0.4843212 1.337876 0.0616281 39 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 70 2000 5 nonlinear linear 0.5890 0.4582245 0.4240406 0.0341839 0.5254017 0.4767601 1.335804 0.7130 0.5860 0.4783331 1.334062 0.0542925 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 71 2000 5 nonlinear linear 0.6600 0.4340285 0.4216203 0.0124081 0.5247266 0.4750279 1.313066 0.8452 0.6243 0.4713465 1.331727 0.0497262 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 72 2000 5 nonlinear linear 0.7320 0.4307671 0.4241966 0.0065705 0.5262732 0.4771112 1.325383 0.8921 0.5535 0.4839716 1.342934 0.0597750 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 73 2000 5 nonlinear linear 0.6815 0.4295224 0.4226271 0.0068953 0.5238020 0.4749978 1.310064 0.8848 0.6126 0.4733901 1.326705 0.0507630 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 74 2000 5 nonlinear linear 0.6650 0.4299716 0.4237301 0.0062414 0.5252083 0.4767049 1.316105 0.8934 0.4925 0.4927639 1.336432 0.0690337 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 75 2000 5 nonlinear linear 0.6790 0.4281289 0.4219326 0.0061963 0.5239954 0.4755866 1.314580 0.8958 0.5901 0.4774206 1.330070 0.0554879 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 76 2000 5 nonlinear linear 0.6165 0.4288541 0.4238821 0.0049720 0.5260876 0.4773598 1.311469 0.9060 0.4932 0.4931875 1.332187 0.0693054 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 77 2000 5 nonlinear linear 0.5955 0.4276355 0.4213165 0.0063190 0.5235060 0.4738130 1.320074 0.8907 0.5636 0.4783867 1.331939 0.0570702 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 78 2000 5 nonlinear linear 0.7060 0.4304745 0.4226928 0.0077816 0.5259483 0.4752276 1.320788 0.8811 0.5715 0.4805138 1.348037 0.0578210 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 79 2000 5 nonlinear linear 0.7180 0.4331984 0.4240845 0.0091140 0.5267401 0.4765384 1.314663 0.8704 0.6163 0.4752049 1.332774 0.0511204 34 0.4 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 80 2000 5 nonlinear linear 0.6920 0.4324488 0.4220601 0.0103887 0.5237051 0.4757851 1.308042 0.8618 0.6207 0.4724550 1.333464 0.0503949 36 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 81 2000 5 nonlinear linear 0.5550 0.4577987 0.4241505 0.0336481 0.5261698 0.4764037 1.324613 0.6986 0.4720 0.4967525 1.334679 0.0726020 36 0.2 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 82 2000 5 nonlinear linear 0.6340 0.4263085 0.4226760 0.0036324 0.5273124 0.4754041 1.317509 0.9224 0.5618 0.4828065 1.337367 0.0601304 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 83 2000 5 nonlinear linear 0.7290 0.4356699 0.4216486 0.0140213 0.5230214 0.4743869 1.320215 0.8300 0.5721 0.4779073 1.330004 0.0562587 40 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 84 2000 5 nonlinear linear 0.6100 0.4311176 0.4255520 0.0055656 0.5276372 0.4779420 1.312924 0.8970 0.5911 0.4806059 1.338988 0.0550540 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 85 2000 5 nonlinear linear 0.6225 0.4319957 0.4241851 0.0078106 0.5265930 0.4766202 1.322088 0.8789 0.5969 0.4773611 1.345042 0.0531760 38 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 86 2000 5 nonlinear linear 0.6600 0.4584395 0.4246541 0.0337855 0.5248630 0.4785387 1.311512 0.7253 0.5449 0.4851153 1.335808 0.0604612 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 87 2000 5 nonlinear linear 0.6455 0.4304770 0.4231411 0.0073359 0.5241470 0.4768017 1.313175 0.8847 0.6304 0.4756094 1.332155 0.0524683 38 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 88 2000 5 nonlinear linear 0.5985 0.4564473 0.4228873 0.0335600 0.5263642 0.4753749 1.319379 0.6992 0.5559 0.4799603 1.330459 0.0570730 30 0.2 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 89 2000 5 nonlinear linear 0.7200 0.4347574 0.4218925 0.0128649 0.5247704 0.4746379 1.322482 0.8448 0.6152 0.4727420 1.338272 0.0508495 38 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 90 2000 5 nonlinear linear 0.5590 0.4449396 0.4222555 0.0226841 0.5244755 0.4753580 1.319765 0.7742 0.6094 0.4737964 1.331873 0.0515409 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 91 2000 5 nonlinear linear 0.6230 0.4391304 0.4243644 0.0147660 0.5262125 0.4762179 1.304915 0.8251 0.5433 0.4826370 1.321773 0.0582726 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 92 2000 5 nonlinear linear 0.6825 0.4298847 0.4228176 0.0070671 0.5252188 0.4754935 1.317331 0.8873 0.5285 0.4860535 1.339168 0.0632359 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 93 2000 5 nonlinear linear 0.6790 0.4300918 0.4251795 0.0049123 0.5242481 0.4791248 1.315746 0.9030 0.5904 0.4790654 1.334160 0.0538858 38 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 94 2000 5 nonlinear linear 0.5330 0.4307967 0.4247227 0.0060739 0.5274773 0.4771027 1.309937 0.8808 0.5817 0.4817977 1.331895 0.0570749 37 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 95 2000 5 nonlinear linear 0.6540 0.4252543 0.4218458 0.0034085 0.5241802 0.4741861 1.311923 0.9291 0.5834 0.4775068 1.336610 0.0556610 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 96 2000 5 nonlinear linear 0.5770 0.4319505 0.4234702 0.0084802 0.5261661 0.4754758 1.315586 0.8666 0.5752 0.4800921 1.331301 0.0566219 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 97 2000 5 nonlinear linear 0.6185 0.4484210 0.4227353 0.0256857 0.5240060 0.4757535 1.324055 0.7484 0.5666 0.4798535 1.338379 0.0571182 40 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 98 2000 5 nonlinear linear 0.6945 0.4295197 0.4243435 0.0051762 0.5275764 0.4762840 1.308496 0.9011 0.5858 0.4799481 1.333962 0.0556047 29 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 99 2000 5 nonlinear linear 0.6680 0.4512130 0.4213482 0.0298648 0.5249410 0.4733842 1.323545 0.7428 0.5510 0.4824525 1.335343 0.0611044 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
9 100 2000 5 nonlinear linear 0.6740 0.4311096 0.4249630 0.0061466 0.5258444 0.4781147 1.306594 0.8907 0.6392 0.4722051 1.331329 0.0472421 39 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
10 1 500 10 nonlinear linear 0.5440 0.4598887 0.4219607 0.0379280 0.5252405 0.4741312 1.337913 0.6743 0.5710 0.4786091 1.349617 0.0566484 28 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 2 500 10 nonlinear linear 0.3965 0.5155426 0.4195503 0.0959923 0.5211968 0.4731464 1.354266 0.4503 0.5033 0.4941103 1.377178 0.0745600 29 0.1 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 3 500 10 nonlinear linear 0.4985 0.4673809 0.4232065 0.0441743 0.5238506 0.4763488 1.344595 0.6309 0.5060 0.4975330 1.400372 0.0743264 27 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 4 500 10 nonlinear linear 0.5310 0.4678263 0.4225224 0.0453039 0.5264334 0.4742958 1.367744 0.6581 0.6427 0.4716246 1.381282 0.0491022 24 0.5 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 5 500 10 nonlinear linear 0.5375 0.4541151 0.4211425 0.0329726 0.5257953 0.4732198 1.338313 0.7172 0.5752 0.4820512 1.367361 0.0609087 27 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 6 500 10 nonlinear linear 0.5395 0.4595138 0.4211337 0.0383801 0.5255128 0.4729629 1.349395 0.6763 0.5780 0.4796398 1.375141 0.0585061 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 7 500 10 nonlinear linear 0.5680 0.4552052 0.4213224 0.0338828 0.5247751 0.4737986 1.336715 0.7367 0.6184 0.4726588 1.362711 0.0513365 22 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 8 500 10 nonlinear linear 0.4170 0.5140948 0.4215813 0.0925135 0.5265183 0.4731802 1.369550 0.4555 0.5429 0.4878535 1.383767 0.0662722 23 0.1 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 9 500 10 nonlinear linear 0.4380 0.4841578 0.4244511 0.0597068 0.5267345 0.4770809 1.393679 0.5696 0.5584 0.4876441 1.431072 0.0631930 23 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 10 500 10 nonlinear linear 0.5675 0.4758544 0.4222772 0.0535772 0.5254877 0.4741784 1.350666 0.5784 0.5998 0.4771871 1.359978 0.0549099 28 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 11 500 10 nonlinear linear 0.5160 0.4795928 0.4219735 0.0576194 0.5254748 0.4747659 1.336088 0.6179 0.5530 0.4857309 1.358040 0.0637574 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 12 500 10 nonlinear linear 0.5890 0.4693845 0.4239412 0.0454433 0.5252431 0.4764825 1.358707 0.6367 0.5064 0.4957710 1.380740 0.0718298 23 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 13 500 10 nonlinear linear 0.5170 0.4645593 0.4222947 0.0422646 0.5259469 0.4739844 1.351995 0.6775 0.5784 0.4824531 1.368236 0.0601584 28 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 14 500 10 nonlinear linear 0.4930 0.4768944 0.4228424 0.0540520 0.5240482 0.4762700 1.368013 0.6032 0.4980 0.4993806 1.405423 0.0765382 25 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 15 500 10 nonlinear linear 0.4545 0.4825223 0.4230773 0.0594450 0.5265204 0.4749723 1.342645 0.5446 0.5441 0.4879771 1.375372 0.0648998 29 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 16 500 10 nonlinear linear 0.5075 0.4682435 0.4215734 0.0466701 0.5257430 0.4733710 1.356333 0.6623 0.5625 0.4838946 1.363991 0.0623212 23 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 17 500 10 nonlinear linear 0.5250 0.4526086 0.4213501 0.0312585 0.5270381 0.4731360 1.343994 0.7336 0.6233 0.4745158 1.363263 0.0531657 22 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 18 500 10 nonlinear linear 0.4455 0.4809412 0.4226950 0.0582462 0.5241653 0.4753856 1.365400 0.5771 0.5879 0.4803958 1.379856 0.0577008 30 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 19 500 10 nonlinear linear 0.4275 0.4900126 0.4221556 0.0678570 0.5247574 0.4748581 1.382133 0.5266 0.5704 0.4796672 1.380398 0.0575116 30 0.2 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 20 500 10 nonlinear linear 0.5295 0.4660130 0.4229990 0.0430140 0.5251163 0.4748800 1.348995 0.6744 0.5466 0.4901807 1.384826 0.0671817 28 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 21 500 10 nonlinear linear 0.2550 0.4833891 0.4237996 0.0595895 0.5264931 0.4760046 1.387856 0.5560 0.5655 0.4782448 1.401103 0.0544452 30 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 22 500 10 nonlinear linear 0.5265 0.4436191 0.4226804 0.0209387 0.5268141 0.4741028 1.333557 0.7849 0.6460 0.4695805 1.371736 0.0469001 23 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 23 500 10 nonlinear linear 0.3575 0.4736949 0.4206139 0.0530810 0.5231108 0.4733179 1.396081 0.5945 0.5695 0.4757284 1.403796 0.0551145 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 24 500 10 nonlinear linear 0.4120 0.4712110 0.4231348 0.0480762 0.5268208 0.4753959 1.363651 0.6326 0.6080 0.4757561 1.361445 0.0526214 25 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 25 500 10 nonlinear linear 0.5210 0.4599358 0.4221012 0.0378346 0.5245872 0.4749934 1.332170 0.7029 0.6213 0.4726592 1.359075 0.0505580 29 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 26 500 10 nonlinear linear 0.5680 0.4756550 0.4237647 0.0518903 0.5248984 0.4761226 1.345994 0.6097 0.5543 0.4852334 1.366059 0.0614687 26 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 27 500 10 nonlinear linear 0.5090 0.4704946 0.4219586 0.0485361 0.5256728 0.4735358 1.361022 0.6163 0.5906 0.4765048 1.370792 0.0545462 29 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 28 500 10 nonlinear linear 0.5325 0.4586883 0.4204314 0.0382569 0.5227710 0.4739980 1.349101 0.6949 0.5719 0.4770791 1.393720 0.0566476 28 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 29 500 10 nonlinear linear 0.4485 0.4685680 0.4236122 0.0449558 0.5265722 0.4756662 1.353248 0.6334 0.5588 0.4823031 1.367979 0.0586909 29 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 30 500 10 nonlinear linear 0.4505 0.4672495 0.4238578 0.0433917 0.5284403 0.4755893 1.364381 0.6607 0.5683 0.4813388 1.397402 0.0574810 27 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 31 500 10 nonlinear linear 0.5395 0.4572358 0.4231990 0.0340367 0.5260617 0.4754717 1.330148 0.7151 0.5888 0.4792790 1.363979 0.0560800 26 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 32 500 10 nonlinear linear 0.4065 0.4744616 0.4225607 0.0519009 0.5227390 0.4762265 1.372196 0.6238 0.6015 0.4765753 1.403529 0.0540146 25 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 33 500 10 nonlinear linear 0.4980 0.4847504 0.4217436 0.0630067 0.5233914 0.4748081 1.408963 0.5500 0.5395 0.4892677 1.413462 0.0675241 28 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 34 500 10 nonlinear linear 0.5570 0.4338090 0.4206034 0.0132056 0.5220830 0.4737978 1.328940 0.8403 0.5496 0.4857220 1.369179 0.0651186 28 0.2 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 35 500 10 nonlinear linear 0.5190 0.4561252 0.4219216 0.0342035 0.5245393 0.4751194 1.332761 0.6996 0.5889 0.4772682 1.355797 0.0553466 26 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 36 500 10 nonlinear linear 0.5275 0.4829479 0.4252081 0.0577398 0.5270706 0.4785788 1.352146 0.6115 0.5840 0.4846176 1.380729 0.0594096 25 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 37 500 10 nonlinear linear 0.5875 0.4680204 0.4214902 0.0465302 0.5249739 0.4738873 1.347803 0.6310 0.5532 0.4840280 1.365846 0.0625378 29 0.3 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 38 500 10 nonlinear linear 0.3505 0.4870961 0.4218908 0.0652054 0.5256305 0.4735906 1.375483 0.5512 0.5373 0.4882779 1.405627 0.0663872 24 0.4 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 39 500 10 nonlinear linear 0.3715 0.4807435 0.4226551 0.0580884 0.5265737 0.4749920 1.372109 0.5812 0.6002 0.4781696 1.394147 0.0555145 26 0.2 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 40 500 10 nonlinear linear 0.4930 0.4714766 0.4212735 0.0502031 0.5247119 0.4736903 1.345857 0.6494 0.6044 0.4775228 1.367245 0.0562492 30 0.6 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 41 500 10 nonlinear linear 0.4085 0.4969271 0.4226206 0.0743065 0.5239942 0.4755224 1.385898 0.5212 0.5252 0.4909656 1.398341 0.0683451 33 0.3 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 42 500 10 nonlinear linear 0.5100 0.4495616 0.4214443 0.0281173 0.5255613 0.4737824 1.342936 0.7599 0.5921 0.4778531 1.371452 0.0564088 27 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 43 500 10 nonlinear linear 0.5360 0.4702647 0.4239807 0.0462840 0.5269076 0.4772429 1.368307 0.6611 0.5683 0.4820354 1.397104 0.0580547 26 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 44 500 10 nonlinear linear 0.5590 0.4486753 0.4224711 0.0262042 0.5240602 0.4752952 1.361128 0.7562 0.4959 0.4983079 1.399071 0.0758368 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 45 500 10 nonlinear linear 0.3940 0.4786749 0.4226678 0.0560071 0.5247795 0.4760453 1.396784 0.5881 0.5691 0.4821497 1.385468 0.0594819 22 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 46 500 10 nonlinear linear 0.4440 0.4688364 0.4223039 0.0465325 0.5228269 0.4764812 1.355841 0.6374 0.5539 0.4851235 1.404985 0.0628195 27 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 47 500 10 nonlinear linear 0.4385 0.4839546 0.4223037 0.0616509 0.5246899 0.4752757 1.346606 0.5684 0.5752 0.4794946 1.356929 0.0571909 28 0.4 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 48 500 10 nonlinear linear 0.3490 0.4629994 0.4236134 0.0393860 0.5255838 0.4759529 1.380387 0.6821 0.5676 0.4815500 1.400943 0.0579366 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 49 500 10 nonlinear linear 0.4200 0.4881701 0.4224379 0.0657322 0.5248552 0.4747715 1.385490 0.5541 0.5759 0.4796904 1.395428 0.0572526 22 0.6 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 50 500 10 nonlinear linear 0.5025 0.4801314 0.4222645 0.0578668 0.5273061 0.4735957 1.366571 0.5984 0.5550 0.4876902 1.382446 0.0654257 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 51 500 10 nonlinear linear 0.5170 0.4992810 0.4254975 0.0737834 0.5253005 0.4791454 1.370801 0.5168 0.5528 0.4875662 1.371568 0.0620686 25 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 52 500 10 nonlinear linear 0.4455 0.4679769 0.4214764 0.0465005 0.5248311 0.4739223 1.356053 0.6716 0.5931 0.4785587 1.392908 0.0570822 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 53 500 10 nonlinear linear 0.4955 0.4738938 0.4243253 0.0495685 0.5271744 0.4775293 1.352418 0.6200 0.5654 0.4836988 1.356123 0.0593735 32 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 54 500 10 nonlinear linear 0.5570 0.4431324 0.4237963 0.0193361 0.5250637 0.4769162 1.354825 0.8034 0.6459 0.4739131 1.378304 0.0501168 26 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 55 500 10 nonlinear linear 0.4510 0.4721732 0.4233827 0.0487905 0.5262218 0.4759429 1.332139 0.5968 0.5683 0.4833224 1.357426 0.0599397 30 0.3 0.7 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 56 500 10 nonlinear linear 0.5080 0.4744148 0.4258646 0.0485502 0.5278767 0.4790035 1.350243 0.6425 0.5951 0.4815092 1.361581 0.0556446 27 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 57 500 10 nonlinear linear 0.3025 0.4862399 0.4228145 0.0634254 0.5251024 0.4760723 1.376689 0.5314 0.5541 0.4825595 1.395077 0.0597451 31 0.1 0.7 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 58 500 10 nonlinear linear 0.5095 0.4788803 0.4233746 0.0555057 0.5265830 0.4756145 1.362999 0.5636 0.5439 0.4884309 1.375738 0.0650563 28 0.1 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 59 500 10 nonlinear linear 0.5195 0.4634776 0.4224022 0.0410754 0.5246509 0.4751691 1.350049 0.6676 0.5480 0.4846378 1.366957 0.0622356 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 60 500 10 nonlinear linear 0.4435 0.4589541 0.4246949 0.0342592 0.5256370 0.4768270 1.339528 0.6967 0.5575 0.4884129 1.351210 0.0637180 26 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 61 500 10 nonlinear linear 0.5520 0.4640056 0.4228617 0.0411439 0.5250542 0.4758308 1.342198 0.7011 0.5493 0.4889462 1.362442 0.0660845 30 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 62 500 10 nonlinear linear 0.4360 0.4469593 0.4220688 0.0248905 0.5231110 0.4762100 1.334986 0.7714 0.5875 0.4784993 1.383075 0.0564305 34 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 63 500 10 nonlinear linear 0.4925 0.4798267 0.4233279 0.0564988 0.5262594 0.4756957 1.368382 0.5717 0.5836 0.4789744 1.395940 0.0556465 25 0.3 0.7 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 64 500 10 nonlinear linear 0.3560 0.4669632 0.4231710 0.0437921 0.5272193 0.4743478 1.372566 0.6463 0.5796 0.4791289 1.396516 0.0559579 25 0.1 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 65 500 10 nonlinear linear 0.5395 0.5084913 0.4218056 0.0866856 0.5247907 0.4730863 1.368769 0.4810 0.5555 0.4862197 1.383231 0.0644141 29 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 66 500 10 nonlinear linear 0.4470 0.4932741 0.4219493 0.0713248 0.5260192 0.4738128 1.367611 0.5024 0.5759 0.4785869 1.352791 0.0566376 24 0.6 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 67 500 10 nonlinear linear 0.4200 0.4709075 0.4222495 0.0486581 0.5255971 0.4749977 1.361082 0.6515 0.5935 0.4770115 1.409113 0.0547621 28 0.4 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 68 500 10 nonlinear linear 0.5375 0.4623774 0.4207693 0.0416081 0.5246576 0.4721279 1.353885 0.6779 0.5987 0.4754185 1.386367 0.0546492 25 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 69 500 10 nonlinear linear 0.4075 0.4804763 0.4208949 0.0595814 0.5254439 0.4732634 1.388325 0.5564 0.5667 0.4824019 1.388645 0.0615070 29 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 70 500 10 nonlinear linear 0.4505 0.5007469 0.4230077 0.0777392 0.5267619 0.4750693 1.378228 0.5025 0.5298 0.4948285 1.379956 0.0718208 25 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 71 500 10 nonlinear linear 0.4965 0.5024860 0.4223580 0.0801280 0.5239552 0.4740691 1.364433 0.5001 0.5799 0.4822615 1.371370 0.0599035 21 0.3 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 72 500 10 nonlinear linear 0.4270 0.4770489 0.4218357 0.0552131 0.5259330 0.4746505 1.353178 0.5872 0.5477 0.4866728 1.374673 0.0648371 27 0.5 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 73 500 10 nonlinear linear 0.3890 0.4798788 0.4230809 0.0567979 0.5263380 0.4758126 1.386393 0.5653 0.5645 0.4843302 1.408655 0.0612493 29 0.3 0.7 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 74 500 10 nonlinear linear 0.5395 0.4478189 0.4219372 0.0258818 0.5241317 0.4742938 1.370569 0.7663 0.5385 0.4937646 1.410395 0.0718274 25 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 75 500 10 nonlinear linear 0.5295 0.4590998 0.4254234 0.0336765 0.5261002 0.4789856 1.341013 0.7116 0.6065 0.4786666 1.390582 0.0532432 26 0.4 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 76 500 10 nonlinear linear 0.4725 0.4503693 0.4225646 0.0278047 0.5242344 0.4760833 1.341620 0.7531 0.6620 0.4691774 1.381788 0.0466128 24 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 77 500 10 nonlinear linear 0.4630 0.4663422 0.4235803 0.0427619 0.5260711 0.4760203 1.347701 0.6813 0.6191 0.4748603 1.362649 0.0512801 28 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 78 500 10 nonlinear linear 0.5075 0.4762188 0.4214039 0.0548150 0.5244434 0.4743465 1.367026 0.5755 0.5673 0.4788030 1.394223 0.0573991 30 0.2 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 79 500 10 nonlinear linear 0.5080 0.5205607 0.4230569 0.0975038 0.5255848 0.4759025 1.342533 0.4485 0.6063 0.4779916 1.348213 0.0549347 29 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 80 500 10 nonlinear linear 0.3605 0.4495969 0.4220135 0.0275834 0.5278423 0.4731807 1.339046 0.7414 0.5400 0.4916489 1.364354 0.0696354 24 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 81 500 10 nonlinear linear 0.4530 0.4848154 0.4210084 0.0638070 0.5239521 0.4737674 1.355648 0.5617 0.5952 0.4777080 1.364073 0.0566996 29 0.5 0.7 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 82 500 10 nonlinear linear 0.4625 0.4748344 0.4227531 0.0520814 0.5238027 0.4760998 1.401477 0.5921 0.5659 0.4802617 1.420234 0.0575086 34 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 83 500 10 nonlinear linear 0.5615 0.4727673 0.4236917 0.0490756 0.5265036 0.4767032 1.365818 0.6449 0.5977 0.4783900 1.377427 0.0546983 30 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 84 500 10 nonlinear linear 0.3780 0.4819362 0.4225730 0.0593632 0.5243738 0.4756782 1.379816 0.5685 0.5864 0.4786527 1.392799 0.0560797 30 0.2 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 85 500 10 nonlinear linear 0.4470 0.4769775 0.4241448 0.0528326 0.5252290 0.4769775 1.373574 0.4335 0.5612 0.4819690 1.382884 0.0578242 28 0.2 0.7 1.0 0.7142857 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 86 500 10 nonlinear linear 0.5450 0.4540477 0.4216155 0.0324322 0.5247210 0.4733870 1.351647 0.7316 0.5850 0.4768519 1.367452 0.0552364 29 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 87 500 10 nonlinear linear 0.5415 0.4420956 0.4224436 0.0196520 0.5257309 0.4747255 1.349851 0.7994 0.5639 0.4849467 1.382593 0.0625032 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 88 500 10 nonlinear linear 0.4355 0.4467611 0.4237995 0.0229617 0.5255789 0.4764145 1.357014 0.7782 0.7126 0.4601256 1.380143 0.0363262 30 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 89 500 10 nonlinear linear 0.4300 0.4739509 0.4208964 0.0530545 0.5242021 0.4732153 1.371322 0.5819 0.5661 0.4822135 1.404659 0.0613171 26 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 90 500 10 nonlinear linear 0.4885 0.4894226 0.4222087 0.0672138 0.5272749 0.4729576 1.356251 0.5502 0.5642 0.4825524 1.373774 0.0603437 32 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 91 500 10 nonlinear linear 0.4310 0.4835412 0.4237016 0.0598396 0.5256123 0.4755844 1.342736 0.5701 0.5796 0.4841824 1.350588 0.0604808 26 0.1 0.7 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 92 500 10 nonlinear linear 0.4070 0.4820256 0.4224464 0.0595792 0.5268872 0.4745426 1.378468 0.5676 0.5634 0.4812553 1.391652 0.0588090 25 0.2 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 93 500 10 nonlinear linear 0.3880 0.4733170 0.4257269 0.0475901 0.5280475 0.4782626 1.363720 0.6367 0.5651 0.4849384 1.349942 0.0592115 25 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 94 500 10 nonlinear linear 0.3380 0.5012240 0.4227135 0.0785105 0.5282057 0.4743877 1.388839 0.4976 0.5390 0.4891456 1.433173 0.0664321 30 0.2 0.4 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 95 500 10 nonlinear linear 0.5395 0.4367796 0.4211853 0.0155943 0.5233685 0.4730772 1.333582 0.8280 0.5920 0.4762101 1.390449 0.0550248 29 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 96 500 10 nonlinear linear 0.5370 0.4544638 0.4245725 0.0298913 0.5275313 0.4768339 1.336688 0.7542 0.5926 0.4796733 1.357133 0.0551008 24 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 97 500 10 nonlinear linear 0.5550 0.4536286 0.4227636 0.0308650 0.5259561 0.4750786 1.348465 0.7334 0.6091 0.4769727 1.366513 0.0542091 28 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 98 500 10 nonlinear linear 0.5305 0.4774536 0.4228899 0.0545637 0.5249648 0.4762192 1.387358 0.6186 0.5873 0.4844970 1.383804 0.0616071 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 99 500 10 nonlinear linear 0.5320 0.4641107 0.4220929 0.0420178 0.5251140 0.4736390 1.334204 0.6817 0.5749 0.4801869 1.369229 0.0580940 28 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
10 100 500 10 nonlinear linear 0.5845 0.4528351 0.4233104 0.0295247 0.5257594 0.4763835 1.340304 0.7305 0.5339 0.4901526 1.391502 0.0668422 23 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=linear
11 1 1000 10 nonlinear linear 0.4160 0.5028995 0.4227769 0.0801226 0.5267742 0.4761060 1.358767 0.4990 0.5527 0.4875642 1.371432 0.0647873 36 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 2 1000 10 nonlinear linear 0.4885 0.4691021 0.4247431 0.0443590 0.5260343 0.4779351 1.345222 0.6353 0.5733 0.4817702 1.355749 0.0570272 31 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 3 1000 10 nonlinear linear 0.5430 0.4534605 0.4245454 0.0289151 0.5258404 0.4767760 1.343779 0.7374 0.5752 0.4824141 1.366088 0.0578687 46 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 4 1000 10 nonlinear linear 0.4615 0.4757460 0.4222999 0.0534461 0.5257503 0.4748700 1.344893 0.5929 0.5726 0.4809811 1.359370 0.0586812 31 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 5 1000 10 nonlinear linear 0.3385 0.4780480 0.4242047 0.0538433 0.5252660 0.4770795 1.330590 0.5806 0.5954 0.4790178 1.334045 0.0548131 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 6 1000 10 nonlinear linear 0.4480 0.4447486 0.4226513 0.0220973 0.5254098 0.4750409 1.329880 0.7716 0.5511 0.4874311 1.362029 0.0647798 32 0.7 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 7 1000 10 nonlinear linear 0.4295 0.4888134 0.4233343 0.0654791 0.5249856 0.4758620 1.353081 0.5737 0.5845 0.4796965 1.363952 0.0563622 33 0.1 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 8 1000 10 nonlinear linear 0.6280 0.4476983 0.4231895 0.0245088 0.5256028 0.4753566 1.332838 0.7630 0.6027 0.4771484 1.355870 0.0539589 32 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 9 1000 10 nonlinear linear 0.3620 0.4438318 0.4242955 0.0195364 0.5290335 0.4757547 1.335210 0.8039 0.6088 0.4769864 1.369063 0.0526909 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 10 1000 10 nonlinear linear 0.5400 0.4440675 0.4229751 0.0210924 0.5267764 0.4749680 1.335526 0.7910 0.5267 0.4946122 1.354446 0.0716371 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 11 1000 10 nonlinear linear 0.5765 0.4444385 0.4216762 0.0227624 0.5241003 0.4752954 1.318334 0.7875 0.6434 0.4691997 1.343191 0.0475235 32 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 12 1000 10 nonlinear linear 0.4280 0.4505107 0.4228560 0.0276547 0.5258805 0.4753182 1.348770 0.7470 0.5734 0.4801341 1.376436 0.0572781 33 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 13 1000 10 nonlinear linear 0.5835 0.4594560 0.4236369 0.0358191 0.5251943 0.4769624 1.336262 0.7109 0.5297 0.4930526 1.354602 0.0694157 37 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 14 1000 10 nonlinear linear 0.5515 0.4452499 0.4218245 0.0234254 0.5250317 0.4746841 1.323608 0.7771 0.5682 0.4797865 1.335067 0.0579620 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 15 1000 10 nonlinear linear 0.5605 0.4635475 0.4224439 0.0411036 0.5254833 0.4743524 1.346962 0.6744 0.5918 0.4780448 1.367164 0.0556009 36 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 16 1000 10 nonlinear linear 0.4520 0.4573226 0.4236333 0.0336893 0.5275880 0.4741343 1.351450 0.7194 0.5568 0.4896631 1.370339 0.0660298 30 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 17 1000 10 nonlinear linear 0.4765 0.4456528 0.4239657 0.0216871 0.5279553 0.4758892 1.332327 0.7881 0.5592 0.4859132 1.359943 0.0619475 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 18 1000 10 nonlinear linear 0.4295 0.4648506 0.4227097 0.0421410 0.5261942 0.4756608 1.328348 0.6600 0.5669 0.4817509 1.345354 0.0590412 40 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 19 1000 10 nonlinear linear 0.4410 0.4284498 0.4219923 0.0064575 0.5245568 0.4750733 1.328306 0.8926 0.5574 0.4871336 1.366348 0.0651413 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 20 1000 10 nonlinear linear 0.4145 0.4674707 0.4236351 0.0438356 0.5273189 0.4760869 1.328375 0.6428 0.5706 0.4801176 1.346212 0.0564825 33 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 21 1000 10 nonlinear linear 0.4945 0.4516879 0.4226010 0.0290869 0.5246740 0.4751356 1.330628 0.7386 0.5692 0.4803859 1.351075 0.0577850 34 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 22 1000 10 nonlinear linear 0.5165 0.4430432 0.4228444 0.0201988 0.5255550 0.4752226 1.321631 0.8004 0.5687 0.4819407 1.342510 0.0590963 40 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 23 1000 10 nonlinear linear 0.4915 0.4468220 0.4239771 0.0228449 0.5264089 0.4764144 1.330386 0.7809 0.5900 0.4787160 1.356750 0.0547389 39 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 24 1000 10 nonlinear linear 0.5180 0.4568447 0.4238725 0.0329722 0.5263677 0.4760347 1.333808 0.7280 0.5908 0.4793145 1.346164 0.0554421 35 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 25 1000 10 nonlinear linear 0.5540 0.4413908 0.4243063 0.0170844 0.5249383 0.4774279 1.330135 0.8089 0.6010 0.4798424 1.349371 0.0555360 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 26 1000 10 nonlinear linear 0.5245 0.4761050 0.4243409 0.0517641 0.5251596 0.4779515 1.316869 0.5732 0.5614 0.4837081 1.345207 0.0593672 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 27 1000 10 nonlinear linear 0.4865 0.4309430 0.4249600 0.0059829 0.5269438 0.4777029 1.326454 0.8928 0.5544 0.4847512 1.358782 0.0597912 27 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 28 1000 10 nonlinear linear 0.5210 0.4607395 0.4203474 0.0403921 0.5231487 0.4722008 1.337556 0.6591 0.5681 0.4757154 1.350267 0.0553681 34 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 29 1000 10 nonlinear linear 0.4440 0.4730140 0.4213654 0.0516486 0.5247760 0.4736645 1.349602 0.6243 0.5889 0.4766367 1.350166 0.0552713 33 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 30 1000 10 nonlinear linear 0.4745 0.4634651 0.4200214 0.0434437 0.5225251 0.4731479 1.352521 0.6569 0.5445 0.4828479 1.362104 0.0628265 34 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 31 1000 10 nonlinear linear 0.5875 0.4461379 0.4226340 0.0235039 0.5263641 0.4752332 1.333854 0.7847 0.5393 0.4867358 1.360489 0.0641018 33 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 32 1000 10 nonlinear linear 0.5290 0.4411927 0.4229804 0.0182123 0.5255945 0.4760782 1.325120 0.8126 0.5624 0.4825122 1.358034 0.0595318 34 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 33 1000 10 nonlinear linear 0.4910 0.4594929 0.4229563 0.0365366 0.5283600 0.4743274 1.342199 0.7017 0.5879 0.4806607 1.354086 0.0577044 31 0.4 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 34 1000 10 nonlinear linear 0.5655 0.4479939 0.4221657 0.0258281 0.5246083 0.4750144 1.332553 0.7581 0.5718 0.4808099 1.350926 0.0586442 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 35 1000 10 nonlinear linear 0.5640 0.4580190 0.4224823 0.0355367 0.5264196 0.4737086 1.326041 0.6896 0.5591 0.4813503 1.335317 0.0588680 39 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 36 1000 10 nonlinear linear 0.5755 0.4542037 0.4237746 0.0304290 0.5254217 0.4763749 1.347528 0.7296 0.5622 0.4800286 1.362531 0.0562540 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 37 1000 10 nonlinear linear 0.4610 0.4784369 0.4222856 0.0561513 0.5265956 0.4743614 1.351262 0.5560 0.5435 0.4838949 1.358624 0.0616092 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 38 1000 10 nonlinear linear 0.5285 0.4368179 0.4240791 0.0127388 0.5277584 0.4759995 1.329458 0.8466 0.5866 0.4806215 1.353716 0.0565424 31 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 39 1000 10 nonlinear linear 0.4580 0.4512860 0.4229009 0.0283851 0.5272337 0.4749139 1.347081 0.7417 0.6107 0.4763640 1.353270 0.0534631 37 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 40 1000 10 nonlinear linear 0.4650 0.4423392 0.4228057 0.0195335 0.5269067 0.4751598 1.328193 0.7989 0.5990 0.4772204 1.348342 0.0544147 38 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 41 1000 10 nonlinear linear 0.5085 0.4561475 0.4237526 0.0323949 0.5268370 0.4764707 1.322934 0.7342 0.5840 0.4851755 1.345582 0.0614229 36 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 42 1000 10 nonlinear linear 0.5655 0.4506415 0.4243345 0.0263070 0.5245625 0.4785677 1.342310 0.7523 0.5592 0.4852054 1.365434 0.0608709 32 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 43 1000 10 nonlinear linear 0.5700 0.4507009 0.4214688 0.0292321 0.5253550 0.4725980 1.334628 0.7368 0.5754 0.4781139 1.356169 0.0566451 29 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 44 1000 10 nonlinear linear 0.5120 0.4351975 0.4223607 0.0128367 0.5265345 0.4743351 1.322884 0.8423 0.5870 0.4792459 1.353829 0.0568852 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 45 1000 10 nonlinear linear 0.4460 0.4563980 0.4242964 0.0321017 0.5274729 0.4765736 1.334897 0.7434 0.5833 0.4801449 1.369984 0.0558485 38 0.1 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 46 1000 10 nonlinear linear 0.4680 0.4598273 0.4229483 0.0368790 0.5246269 0.4757478 1.330460 0.6778 0.5649 0.4791312 1.354669 0.0561829 37 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 47 1000 10 nonlinear linear 0.4960 0.4457003 0.4220755 0.0236248 0.5251259 0.4745246 1.326158 0.7760 0.5747 0.4801394 1.341945 0.0580639 33 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 48 1000 10 nonlinear linear 0.5070 0.4370196 0.4256373 0.0113823 0.5265236 0.4781835 1.318664 0.8502 0.6385 0.4738597 1.342829 0.0482224 32 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 49 1000 10 nonlinear linear 0.5645 0.4558619 0.4225426 0.0333193 0.5273916 0.4746814 1.335129 0.7325 0.5573 0.4862331 1.353388 0.0636905 39 0.4 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 50 1000 10 nonlinear linear 0.4955 0.4361054 0.4233202 0.0127852 0.5258624 0.4748746 1.330225 0.8428 0.5368 0.4909985 1.357524 0.0676783 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 51 1000 10 nonlinear linear 0.5125 0.4505564 0.4231789 0.0273775 0.5258405 0.4750953 1.326791 0.7615 0.5873 0.4777383 1.350370 0.0545595 34 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 52 1000 10 nonlinear linear 0.3830 0.4620359 0.4219589 0.0400770 0.5239845 0.4747135 1.347075 0.6621 0.5741 0.4771366 1.356048 0.0551777 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 53 1000 10 nonlinear linear 0.4985 0.4485587 0.4242599 0.0242988 0.5268738 0.4767173 1.340292 0.7695 0.5551 0.4895065 1.370776 0.0652467 30 0.3 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 54 1000 10 nonlinear linear 0.5330 0.4400744 0.4198017 0.0202727 0.5218926 0.4724301 1.324801 0.7919 0.5862 0.4747753 1.339901 0.0549736 34 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 55 1000 10 nonlinear linear 0.5700 0.4709507 0.4225037 0.0484470 0.5260233 0.4745347 1.341376 0.6201 0.5840 0.4780299 1.341097 0.0555262 38 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 56 1000 10 nonlinear linear 0.5525 0.4539456 0.4208496 0.0330960 0.5247667 0.4723721 1.340191 0.7448 0.6190 0.4714222 1.354253 0.0505725 43 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 57 1000 10 nonlinear linear 0.4555 0.4387087 0.4235927 0.0151159 0.5248663 0.4761050 1.324213 0.8223 0.5785 0.4789515 1.347481 0.0553588 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 58 1000 10 nonlinear linear 0.5705 0.4914608 0.4228319 0.0686289 0.5255661 0.4751416 1.332842 0.5044 0.5719 0.4795914 1.341819 0.0567595 30 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 59 1000 10 nonlinear linear 0.4260 0.4857795 0.4236935 0.0620861 0.5271330 0.4758382 1.360703 0.5900 0.6336 0.4734336 1.361395 0.0497402 33 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 60 1000 10 nonlinear linear 0.5080 0.4763210 0.4239266 0.0523944 0.5268831 0.4752640 1.356127 0.5959 0.5575 0.4823706 1.359789 0.0584440 33 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 61 1000 10 nonlinear linear 0.5095 0.4597903 0.4261226 0.0336677 0.5284292 0.4786679 1.347077 0.7117 0.6125 0.4787699 1.359943 0.0526472 31 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 62 1000 10 nonlinear linear 0.5610 0.4437239 0.4246039 0.0191200 0.5259072 0.4772987 1.329488 0.8007 0.5882 0.4799802 1.344605 0.0553763 29 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 63 1000 10 nonlinear linear 0.3930 0.4391762 0.4218474 0.0173288 0.5264690 0.4744034 1.323672 0.8107 0.5748 0.4787835 1.353504 0.0569361 33 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 64 1000 10 nonlinear linear 0.5445 0.4307939 0.4228268 0.0079671 0.5255365 0.4751171 1.324519 0.8782 0.5721 0.4814176 1.343388 0.0585908 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 65 1000 10 nonlinear linear 0.3560 0.4682711 0.4224310 0.0458401 0.5251868 0.4751581 1.329228 0.6225 0.5787 0.4788299 1.347506 0.0563989 33 0.1 0.2 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 66 1000 10 nonlinear linear 0.5250 0.4415303 0.4235914 0.0179389 0.5267842 0.4761998 1.336912 0.8334 0.6336 0.4746104 1.352395 0.0510190 36 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 67 1000 10 nonlinear linear 0.4215 0.4428818 0.4212581 0.0216237 0.5269261 0.4726800 1.326124 0.7818 0.5599 0.4802734 1.339861 0.0590153 34 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 68 1000 10 nonlinear linear 0.5190 0.4701067 0.4199726 0.0501341 0.5229203 0.4722860 1.341526 0.6251 0.5796 0.4773455 1.350972 0.0573730 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 69 1000 10 nonlinear linear 0.4860 0.4486026 0.4224598 0.0261428 0.5260844 0.4746051 1.329108 0.7551 0.6060 0.4760557 1.355914 0.0535960 30 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 70 1000 10 nonlinear linear 0.4025 0.4658225 0.4219084 0.0439141 0.5245670 0.4746950 1.343023 0.6419 0.5658 0.4796410 1.348168 0.0577326 31 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 71 1000 10 nonlinear linear 0.5260 0.4501436 0.4226805 0.0274631 0.5259269 0.4752608 1.324383 0.7471 0.5887 0.4781033 1.343867 0.0554229 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 72 1000 10 nonlinear linear 0.4910 0.4439583 0.4226016 0.0213567 0.5243308 0.4766368 1.331902 0.7901 0.5612 0.4810984 1.360862 0.0584968 32 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 73 1000 10 nonlinear linear 0.4390 0.4561471 0.4223767 0.0337704 0.5263527 0.4744309 1.338094 0.7012 0.5833 0.4785907 1.366080 0.0562139 29 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 74 1000 10 nonlinear linear 0.5315 0.4824808 0.4248561 0.0576247 0.5277139 0.4771653 1.338603 0.5648 0.5578 0.4872939 1.349269 0.0624378 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 75 1000 10 nonlinear linear 0.5130 0.4554493 0.4239874 0.0314619 0.5274262 0.4756883 1.344460 0.7399 0.6049 0.4787785 1.366701 0.0547912 38 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 76 1000 10 nonlinear linear 0.4535 0.4986830 0.4231361 0.0755470 0.5251763 0.4772803 1.358145 0.5123 0.5607 0.4854862 1.363690 0.0623501 36 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 77 1000 10 nonlinear linear 0.5115 0.4389881 0.4235575 0.0154307 0.5264394 0.4759385 1.328472 0.8273 0.5527 0.4865449 1.346692 0.0629874 27 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 78 1000 10 nonlinear linear 0.4185 0.4653112 0.4239397 0.0413716 0.5240048 0.4778945 1.351306 0.6780 0.5619 0.4836255 1.358388 0.0596858 41 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 79 1000 10 nonlinear linear 0.4090 0.4734638 0.4234791 0.0499848 0.5270912 0.4754874 1.343311 0.6104 0.5799 0.4802412 1.349236 0.0567621 38 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 80 1000 10 nonlinear linear 0.4290 0.4659758 0.4241459 0.0418299 0.5266759 0.4776876 1.330546 0.6912 0.6612 0.4689583 1.345017 0.0448123 37 0.2 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 81 1000 10 nonlinear linear 0.6250 0.4414442 0.4212619 0.0201823 0.5261250 0.4730516 1.335967 0.7980 0.5650 0.4800122 1.354556 0.0587503 34 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 82 1000 10 nonlinear linear 0.5085 0.4650073 0.4225030 0.0425042 0.5258434 0.4744939 1.353997 0.6566 0.5526 0.4819049 1.352178 0.0594018 30 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 83 1000 10 nonlinear linear 0.4240 0.5069851 0.4221290 0.0848561 0.5261845 0.4747885 1.336861 0.4885 0.5369 0.4920338 1.344313 0.0699049 31 0.1 0.7 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 84 1000 10 nonlinear linear 0.5070 0.4748988 0.4209598 0.0539390 0.5249389 0.4730922 1.350525 0.5917 0.5792 0.4791674 1.354316 0.0582076 36 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 85 1000 10 nonlinear linear 0.5575 0.4501529 0.4232771 0.0268758 0.5261944 0.4762988 1.312186 0.7566 0.5486 0.4841865 1.325611 0.0609094 35 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 86 1000 10 nonlinear linear 0.4835 0.4592505 0.4208654 0.0383852 0.5229166 0.4731200 1.335272 0.6757 0.5840 0.4757343 1.340286 0.0548689 31 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 87 1000 10 nonlinear linear 0.4195 0.4602242 0.4232476 0.0369766 0.5274890 0.4742269 1.327669 0.7101 0.5702 0.4818030 1.350639 0.0585554 27 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 88 1000 10 nonlinear linear 0.4825 0.4320559 0.4238287 0.0082272 0.5257935 0.4771236 1.325445 0.8755 0.5717 0.4833077 1.359469 0.0594790 41 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 89 1000 10 nonlinear linear 0.5705 0.4571870 0.4248324 0.0323547 0.5252928 0.4774397 1.326130 0.7130 0.5861 0.4787620 1.331318 0.0539297 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 90 1000 10 nonlinear linear 0.4050 0.4795742 0.4225615 0.0570126 0.5251746 0.4749934 1.362000 0.5909 0.6144 0.4754583 1.355512 0.0528968 35 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 91 1000 10 nonlinear linear 0.4985 0.4612506 0.4221346 0.0391160 0.5273071 0.4742714 1.341070 0.6725 0.5246 0.4871130 1.351295 0.0649784 38 0.4 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 92 1000 10 nonlinear linear 0.5325 0.4712534 0.4238200 0.0474334 0.5261710 0.4764237 1.334348 0.6174 0.5429 0.4848425 1.336416 0.0610225 32 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 93 1000 10 nonlinear linear 0.3520 0.4529478 0.4218054 0.0311423 0.5241604 0.4746043 1.336475 0.7220 0.6080 0.4739203 1.353289 0.0521149 31 0.3 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 94 1000 10 nonlinear linear 0.4660 0.4590631 0.4228216 0.0362415 0.5255758 0.4753577 1.330293 0.7187 0.6096 0.4742899 1.353479 0.0514683 38 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 95 1000 10 nonlinear linear 0.4955 0.4462687 0.4231138 0.0231549 0.5257832 0.4756965 1.334540 0.7695 0.6112 0.4750442 1.354345 0.0519304 31 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 96 1000 10 nonlinear linear 0.5120 0.4495814 0.4238563 0.0257251 0.5278866 0.4753992 1.338357 0.7571 0.5728 0.4826296 1.368397 0.0587733 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 97 1000 10 nonlinear linear 0.4875 0.4476244 0.4245664 0.0230580 0.5267806 0.4778377 1.328213 0.7781 0.5351 0.4871878 1.339337 0.0626214 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 98 1000 10 nonlinear linear 0.6105 0.4369644 0.4231126 0.0138518 0.5270580 0.4750147 1.320885 0.8381 0.5780 0.4835662 1.349156 0.0604535 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 99 1000 10 nonlinear linear 0.5120 0.4709257 0.4221133 0.0488124 0.5244576 0.4744788 1.338795 0.6629 0.5998 0.4773581 1.351058 0.0552448 34 0.3 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
11 100 1000 10 nonlinear linear 0.4740 0.4957423 0.4221412 0.0736012 0.5256831 0.4741697 1.349937 0.5202 0.5677 0.4815722 1.354483 0.0594310 36 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=linear
12 1 2000 10 nonlinear linear 0.3805 0.4510966 0.4213804 0.0297162 0.5233022 0.4741269 1.334784 0.7444 0.5398 0.4828177 1.344132 0.0614373 51 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 2 2000 10 nonlinear linear 0.4610 0.4370794 0.4247810 0.0122984 0.5276594 0.4771400 1.317023 0.8467 0.5870 0.4795860 1.342722 0.0548051 39 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 3 2000 10 nonlinear linear 0.4680 0.4332886 0.4198498 0.0134387 0.5218856 0.4723212 1.324875 0.8329 0.5762 0.4758386 1.347456 0.0559887 40 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 4 2000 10 nonlinear linear 0.5820 0.4427570 0.4229733 0.0197837 0.5262236 0.4748400 1.320581 0.7971 0.5729 0.4823210 1.335250 0.0593476 41 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 5 2000 10 nonlinear linear 0.4075 0.4332640 0.4219160 0.0113480 0.5247725 0.4743571 1.309398 0.8538 0.6008 0.4748724 1.327664 0.0529563 37 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 6 2000 10 nonlinear linear 0.5090 0.4537217 0.4203259 0.0333958 0.5232587 0.4733172 1.327994 0.7020 0.5907 0.4752601 1.347991 0.0549342 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 7 2000 10 nonlinear linear 0.5220 0.4370072 0.4214569 0.0155503 0.5246643 0.4741489 1.331018 0.8249 0.6284 0.4711825 1.346864 0.0497256 38 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 8 2000 10 nonlinear linear 0.4800 0.4329461 0.4205114 0.0124347 0.5243211 0.4727154 1.314867 0.8445 0.6208 0.4708121 1.331720 0.0503007 40 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 9 2000 10 nonlinear linear 0.4670 0.4373992 0.4235950 0.0138042 0.5265034 0.4752408 1.316516 0.8326 0.6284 0.4723295 1.334699 0.0487345 40 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 10 2000 10 nonlinear linear 0.4595 0.4328228 0.4232134 0.0096094 0.5239811 0.4766357 1.324525 0.8640 0.5784 0.4794953 1.352268 0.0562819 41 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 11 2000 10 nonlinear linear 0.3870 0.4332329 0.4226851 0.0105478 0.5251685 0.4753604 1.314874 0.8583 0.5946 0.4766746 1.341466 0.0539895 41 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 12 2000 10 nonlinear linear 0.6195 0.4277292 0.4214223 0.0063069 0.5249636 0.4746376 1.322487 0.8941 0.6742 0.4629365 1.339822 0.0415142 49 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 13 2000 10 nonlinear linear 0.5505 0.4329243 0.4221912 0.0107332 0.5254860 0.4732810 1.310057 0.8593 0.6232 0.4727614 1.334854 0.0505703 40 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 14 2000 10 nonlinear linear 0.5345 0.4433476 0.4221287 0.0212189 0.5239947 0.4754658 1.335006 0.7954 0.6084 0.4747012 1.349068 0.0525725 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 15 2000 10 nonlinear linear 0.5620 0.4409832 0.4256964 0.0152868 0.5285870 0.4782543 1.313622 0.8212 0.5640 0.4837344 1.334277 0.0580380 41 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 16 2000 10 nonlinear linear 0.4045 0.4465687 0.4217997 0.0247690 0.5248084 0.4741682 1.321798 0.7561 0.5674 0.4791159 1.339810 0.0573162 34 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 17 2000 10 nonlinear linear 0.5125 0.4310436 0.4236456 0.0073980 0.5282387 0.4758847 1.320302 0.8861 0.5942 0.4791392 1.343026 0.0554936 38 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 18 2000 10 nonlinear linear 0.5450 0.4479198 0.4232249 0.0246949 0.5261464 0.4759901 1.322135 0.7653 0.5892 0.4772126 1.334986 0.0539877 45 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 19 2000 10 nonlinear linear 0.5680 0.4417962 0.4236509 0.0181454 0.5242152 0.4781420 1.327436 0.8092 0.5873 0.4789279 1.343144 0.0552771 39 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 20 2000 10 nonlinear linear 0.5350 0.4343247 0.4223947 0.0119300 0.5256141 0.4747812 1.318248 0.8479 0.6092 0.4745019 1.336761 0.0521072 39 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 21 2000 10 nonlinear linear 0.4650 0.4330140 0.4224758 0.0105382 0.5248959 0.4748912 1.321978 0.8573 0.5587 0.4809543 1.345452 0.0584785 38 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 22 2000 10 nonlinear linear 0.5125 0.4391604 0.4230634 0.0160971 0.5239753 0.4767204 1.326513 0.8277 0.6001 0.4761646 1.350469 0.0531012 39 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 23 2000 10 nonlinear linear 0.5425 0.4435490 0.4227828 0.0207662 0.5256020 0.4753393 1.325578 0.7903 0.5435 0.4839660 1.343910 0.0611832 47 0.6 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 24 2000 10 nonlinear linear 0.5270 0.4448198 0.4228321 0.0219877 0.5263674 0.4749961 1.329849 0.7766 0.5928 0.4774488 1.345070 0.0546167 36 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 25 2000 10 nonlinear linear 0.5095 0.4454728 0.4232012 0.0222716 0.5258558 0.4763324 1.311181 0.7759 0.6311 0.4719897 1.329845 0.0487886 35 0.4 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 26 2000 10 nonlinear linear 0.5165 0.4558519 0.4255294 0.0303225 0.5277037 0.4770864 1.326539 0.7305 0.5626 0.4842906 1.340864 0.0587612 38 0.3 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 27 2000 10 nonlinear linear 0.4975 0.4389651 0.4250711 0.0138940 0.5269320 0.4777794 1.315292 0.8341 0.5798 0.4815145 1.337428 0.0564434 35 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 28 2000 10 nonlinear linear 0.4480 0.4382412 0.4228900 0.0153513 0.5252600 0.4757236 1.318056 0.8333 0.6312 0.4717568 1.335057 0.0488669 43 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 29 2000 10 nonlinear linear 0.4740 0.4342876 0.4222576 0.0120300 0.5247828 0.4751551 1.317192 0.8510 0.6535 0.4677246 1.335925 0.0454670 40 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 30 2000 10 nonlinear linear 0.4805 0.4308493 0.4210106 0.0098387 0.5244222 0.4736912 1.317055 0.8641 0.6420 0.4681500 1.344770 0.0471395 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 31 2000 10 nonlinear linear 0.5375 0.4338269 0.4237019 0.0101250 0.5274570 0.4749912 1.317026 0.8674 0.6251 0.4738128 1.342421 0.0501108 44 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 32 2000 10 nonlinear linear 0.4775 0.4325789 0.4229441 0.0096349 0.5259046 0.4753429 1.314759 0.8629 0.5765 0.4790967 1.340020 0.0561526 38 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 33 2000 10 nonlinear linear 0.4860 0.4502049 0.4256793 0.0245256 0.5277931 0.4775787 1.331646 0.7664 0.6154 0.4767879 1.346265 0.0511086 42 0.3 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 34 2000 10 nonlinear linear 0.5170 0.4432339 0.4230005 0.0202335 0.5268091 0.4745760 1.319607 0.7869 0.5841 0.4789709 1.342384 0.0559705 40 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 35 2000 10 nonlinear linear 0.4970 0.4333570 0.4241278 0.0092293 0.5273218 0.4766601 1.320844 0.8688 0.5920 0.4785855 1.343158 0.0544578 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 36 2000 10 nonlinear linear 0.5425 0.4470461 0.4226327 0.0244134 0.5246442 0.4752284 1.319356 0.7621 0.5797 0.4808594 1.338511 0.0582267 44 0.3 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 37 2000 10 nonlinear linear 0.4680 0.4380127 0.4239254 0.0140873 0.5265579 0.4761802 1.318005 0.8329 0.6175 0.4742555 1.336229 0.0503301 39 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 38 2000 10 nonlinear linear 0.4125 0.4380801 0.4220107 0.0160694 0.5250195 0.4741804 1.319565 0.8172 0.5901 0.4773673 1.340858 0.0553565 37 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 39 2000 10 nonlinear linear 0.4775 0.4439460 0.4217572 0.0221888 0.5255327 0.4747062 1.319134 0.7858 0.5750 0.4792577 1.339395 0.0575006 32 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 40 2000 10 nonlinear linear 0.4890 0.4464840 0.4232686 0.0232155 0.5267554 0.4759325 1.321384 0.7737 0.5717 0.4787767 1.348650 0.0555082 43 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 41 2000 10 nonlinear linear 0.5285 0.4516480 0.4198322 0.0318158 0.5222738 0.4732549 1.332377 0.7385 0.5823 0.4780364 1.354244 0.0582041 39 0.8 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 42 2000 10 nonlinear linear 0.5040 0.4358124 0.4219834 0.0138290 0.5257285 0.4742611 1.327080 0.8398 0.5820 0.4786480 1.347376 0.0566646 39 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 43 2000 10 nonlinear linear 0.3580 0.4407471 0.4229083 0.0178388 0.5256886 0.4749407 1.326617 0.8102 0.6025 0.4768331 1.341129 0.0539248 37 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 44 2000 10 nonlinear linear 0.4920 0.4764034 0.4233467 0.0530566 0.5245836 0.4764179 1.338683 0.5656 0.5321 0.4851217 1.340006 0.0617750 46 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 45 2000 10 nonlinear linear 0.5965 0.4347664 0.4222892 0.0124773 0.5259071 0.4744404 1.315962 0.8421 0.6281 0.4718859 1.341444 0.0495967 43 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 46 2000 10 nonlinear linear 0.5375 0.4351638 0.4220941 0.0130697 0.5246658 0.4745681 1.323611 0.8438 0.5665 0.4796292 1.347528 0.0575351 46 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 47 2000 10 nonlinear linear 0.5200 0.4491696 0.4222594 0.0269102 0.5247926 0.4754029 1.330329 0.7621 0.5780 0.4787853 1.336456 0.0565259 42 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 48 2000 10 nonlinear linear 0.4615 0.4483202 0.4226451 0.0256750 0.5270161 0.4745374 1.318229 0.7651 0.5723 0.4802876 1.336010 0.0576424 43 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 49 2000 10 nonlinear linear 0.5080 0.4419027 0.4235530 0.0183497 0.5265024 0.4764802 1.318057 0.8067 0.5664 0.4817657 1.334416 0.0582127 45 0.5 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 50 2000 10 nonlinear linear 0.5275 0.4395744 0.4238757 0.0156987 0.5259904 0.4769920 1.318565 0.8238 0.5613 0.4829814 1.334640 0.0591057 37 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 51 2000 10 nonlinear linear 0.5470 0.4491611 0.4237115 0.0254496 0.5260459 0.4761318 1.316629 0.7937 0.6244 0.4727234 1.342726 0.0490120 38 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 52 2000 10 nonlinear linear 0.4780 0.4300877 0.4228427 0.0072450 0.5256404 0.4753606 1.318314 0.8879 0.5939 0.4770765 1.340612 0.0542338 43 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 53 2000 10 nonlinear linear 0.5485 0.4363879 0.4214607 0.0149272 0.5248579 0.4737464 1.319739 0.8305 0.6158 0.4726764 1.334413 0.0512157 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 54 2000 10 nonlinear linear 0.5405 0.4405484 0.4239293 0.0166191 0.5263174 0.4769321 1.320415 0.8197 0.5721 0.4807463 1.336389 0.0568170 41 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 55 2000 10 nonlinear linear 0.4965 0.4360627 0.4216084 0.0144544 0.5251553 0.4740427 1.316030 0.8303 0.5714 0.4783260 1.340160 0.0567176 43 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 56 2000 10 nonlinear linear 0.4140 0.4555306 0.4246406 0.0308900 0.5283387 0.4757988 1.324298 0.7422 0.6040 0.4775256 1.341555 0.0528851 40 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 57 2000 10 nonlinear linear 0.4535 0.4392113 0.4235970 0.0156143 0.5252012 0.4758556 1.314439 0.8231 0.5830 0.4789913 1.338011 0.0553942 45 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 58 2000 10 nonlinear linear 0.5235 0.4439855 0.4224471 0.0215384 0.5247979 0.4749124 1.336534 0.7811 0.5617 0.4800150 1.354265 0.0575679 37 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 59 2000 10 nonlinear linear 0.3835 0.4545662 0.4240590 0.0305072 0.5249210 0.4779123 1.333986 0.7257 0.5780 0.4809716 1.354749 0.0569126 35 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 60 2000 10 nonlinear linear 0.5505 0.4636378 0.4222569 0.0413809 0.5263905 0.4734269 1.314995 0.6540 0.5873 0.4774923 1.337049 0.0552354 41 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 61 2000 10 nonlinear linear 0.5090 0.4346732 0.4220851 0.0125880 0.5269666 0.4739733 1.318368 0.8492 0.5594 0.4817432 1.339333 0.0596581 43 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 62 2000 10 nonlinear linear 0.5225 0.4440858 0.4230950 0.0209908 0.5276219 0.4755059 1.309656 0.7830 0.5432 0.4848406 1.332558 0.0617456 37 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 63 2000 10 nonlinear linear 0.4700 0.4444909 0.4221675 0.0223234 0.5250799 0.4739800 1.314640 0.7761 0.5821 0.4776961 1.333343 0.0555286 50 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 64 2000 10 nonlinear linear 0.5160 0.4312510 0.4205537 0.0106974 0.5276391 0.4712738 1.314768 0.8574 0.6105 0.4740275 1.331190 0.0534738 45 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 65 2000 10 nonlinear linear 0.5090 0.4568374 0.4251089 0.0317285 0.5260635 0.4771487 1.326441 0.7045 0.5247 0.4871409 1.341587 0.0620320 44 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 66 2000 10 nonlinear linear 0.5370 0.4750937 0.4221156 0.0529781 0.5256898 0.4750674 1.323855 0.5724 0.5552 0.4815624 1.341776 0.0594468 34 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 67 2000 10 nonlinear linear 0.5555 0.4338295 0.4208392 0.0129903 0.5246463 0.4728683 1.310131 0.8430 0.5971 0.4745805 1.335991 0.0537414 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 68 2000 10 nonlinear linear 0.5635 0.4316581 0.4231121 0.0085460 0.5263801 0.4752579 1.316430 0.8693 0.6212 0.4738173 1.338069 0.0507052 37 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 69 2000 10 nonlinear linear 0.4350 0.4353626 0.4216777 0.0136849 0.5245936 0.4734105 1.318107 0.8346 0.5684 0.4782408 1.336341 0.0565631 41 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 70 2000 10 nonlinear linear 0.5005 0.4532454 0.4256700 0.0275754 0.5252570 0.4807720 1.340103 0.7454 0.6485 0.4722562 1.350332 0.0465862 39 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 71 2000 10 nonlinear linear 0.5655 0.4350633 0.4221091 0.0129543 0.5247483 0.4747761 1.325544 0.8437 0.6268 0.4714762 1.345262 0.0493671 44 0.7 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 72 2000 10 nonlinear linear 0.5380 0.4384508 0.4229779 0.0154730 0.5255724 0.4756762 1.313557 0.8252 0.6133 0.4741134 1.330924 0.0511356 38 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 73 2000 10 nonlinear linear 0.3745 0.4513740 0.4239657 0.0274083 0.5249207 0.4768549 1.322595 0.7506 0.6230 0.4743130 1.339361 0.0503473 40 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 74 2000 10 nonlinear linear 0.4845 0.4305602 0.4223714 0.0081887 0.5263975 0.4755888 1.313823 0.8773 0.5442 0.4848942 1.329952 0.0625227 37 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 75 2000 10 nonlinear linear 0.4425 0.4354406 0.4225214 0.0129192 0.5260286 0.4745579 1.321684 0.8354 0.5823 0.4783071 1.342595 0.0557857 44 0.5 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 76 2000 10 nonlinear linear 0.5230 0.4496463 0.4219119 0.0277344 0.5261245 0.4736806 1.318191 0.7522 0.5898 0.4766958 1.331632 0.0547839 36 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 77 2000 10 nonlinear linear 0.4895 0.4419584 0.4224265 0.0195320 0.5263089 0.4743142 1.319486 0.8009 0.6043 0.4775215 1.339402 0.0550950 37 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 78 2000 10 nonlinear linear 0.5585 0.4423011 0.4231749 0.0191262 0.5266851 0.4754897 1.325399 0.8025 0.5917 0.4780195 1.346762 0.0548446 38 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 79 2000 10 nonlinear linear 0.3160 0.4481249 0.4211636 0.0269613 0.5250340 0.4731219 1.322658 0.7549 0.5837 0.4772637 1.337632 0.0561001 38 0.6 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 80 2000 10 nonlinear linear 0.5390 0.4607584 0.4256414 0.0351171 0.5271340 0.4794137 1.328364 0.7343 0.6003 0.4809171 1.337334 0.0552758 34 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 81 2000 10 nonlinear linear 0.4695 0.4432141 0.4227984 0.0204157 0.5246822 0.4760107 1.328791 0.8021 0.5834 0.4785608 1.340021 0.0557624 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 82 2000 10 nonlinear linear 0.5405 0.4393554 0.4226917 0.0166637 0.5240677 0.4756912 1.317023 0.8016 0.5801 0.4779510 1.333560 0.0552593 39 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 83 2000 10 nonlinear linear 0.5145 0.4370669 0.4233794 0.0136875 0.5277728 0.4748385 1.310482 0.8352 0.5959 0.4773198 1.331292 0.0539404 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 84 2000 10 nonlinear linear 0.4870 0.4352585 0.4238178 0.0114407 0.5262828 0.4763486 1.321103 0.8525 0.6190 0.4750690 1.344996 0.0512512 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 85 2000 10 nonlinear linear 0.4790 0.4403558 0.4245718 0.0157840 0.5273396 0.4780238 1.316589 0.8284 0.5798 0.4814384 1.337917 0.0568666 47 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 86 2000 10 nonlinear linear 0.5240 0.4390436 0.4222890 0.0167547 0.5262986 0.4739738 1.324721 0.8211 0.6048 0.4739741 1.335115 0.0516851 41 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 87 2000 10 nonlinear linear 0.5340 0.4288505 0.4228675 0.0059830 0.5254587 0.4743706 1.321566 0.8924 0.6401 0.4691181 1.344900 0.0462506 42 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 88 2000 10 nonlinear linear 0.4980 0.4292234 0.4231978 0.0060256 0.5247171 0.4764606 1.314111 0.8971 0.6080 0.4747625 1.337827 0.0515646 38 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 89 2000 10 nonlinear linear 0.5380 0.4432275 0.4228316 0.0203958 0.5254391 0.4743577 1.322060 0.7935 0.6399 0.4694637 1.338971 0.0466321 36 0.1 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 90 2000 10 nonlinear linear 0.4915 0.4477347 0.4248199 0.0229148 0.5267372 0.4776560 1.334051 0.7766 0.6232 0.4741364 1.346876 0.0493165 42 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 91 2000 10 nonlinear linear 0.5340 0.4354155 0.4227028 0.0127127 0.5266427 0.4734601 1.313284 0.8433 0.6120 0.4747825 1.332527 0.0520797 45 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 92 2000 10 nonlinear linear 0.5125 0.4388600 0.4227725 0.0160875 0.5262038 0.4753939 1.322002 0.8243 0.5842 0.4785879 1.342483 0.0558154 38 0.3 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 93 2000 10 nonlinear linear 0.5135 0.4271986 0.4235015 0.0036971 0.5267227 0.4752164 1.311273 0.9198 0.5879 0.4782814 1.341034 0.0547799 40 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 94 2000 10 nonlinear linear 0.5140 0.4553142 0.4219536 0.0333606 0.5249356 0.4746922 1.336560 0.7185 0.5870 0.4778922 1.351488 0.0559386 41 0.7 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 95 2000 10 nonlinear linear 0.3470 0.4648877 0.4246497 0.0402380 0.5258384 0.4782115 1.334889 0.6747 0.5826 0.4816580 1.344120 0.0570083 39 0.4 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 96 2000 10 nonlinear linear 0.4100 0.4282038 0.4209371 0.0072666 0.5245427 0.4733289 1.324245 0.8857 0.5622 0.4782910 1.339446 0.0573539 43 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 97 2000 10 nonlinear linear 0.5425 0.4321700 0.4227151 0.0094549 0.5260068 0.4751724 1.327185 0.8695 0.5724 0.4809625 1.358033 0.0582474 43 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 98 2000 10 nonlinear linear 0.3845 0.4463373 0.4241973 0.0221401 0.5257150 0.4773478 1.331724 0.7881 0.5618 0.4821065 1.339817 0.0579093 34 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 99 2000 10 nonlinear linear 0.3695 0.4377608 0.4235716 0.0141892 0.5276386 0.4749160 1.317475 0.8306 0.5918 0.4794327 1.336291 0.0558611 41 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
12 100 2000 10 nonlinear linear 0.5485 0.4328777 0.4238755 0.0090022 0.5278242 0.4754656 1.310111 0.8709 0.5902 0.4786376 1.330838 0.0547621 42 0.7 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=linear
13 1 500 5 linear nonlinear 0.7395 0.4758926 0.4356202 0.0402724 0.5014336 0.4996916 1.398327 0.6153 0.7823 0.4503875 1.372695 0.0147673 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 2 500 5 linear nonlinear 0.7325 0.4764110 0.4352256 0.0411854 0.5008057 0.5013730 1.387784 0.6197 0.7995 0.4481788 1.365015 0.0129532 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 3 500 5 linear nonlinear 0.7150 0.4617444 0.4343608 0.0273836 0.4984845 0.5000916 1.391940 0.6971 0.7519 0.4527363 1.388595 0.0183756 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 4 500 5 linear nonlinear 0.6270 0.4898832 0.4351626 0.0547206 0.4992240 0.5019036 1.383013 0.5256 0.7624 0.4523047 1.377938 0.0171422 22 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 5 500 5 linear nonlinear 0.6855 0.4660074 0.4362404 0.0297670 0.5007261 0.5028684 1.410631 0.6822 0.8738 0.4410706 1.372823 0.0048303 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 6 500 5 linear nonlinear 0.7185 0.4690667 0.4318021 0.0372646 0.4968863 0.4990555 1.378867 0.7124 0.8681 0.4373783 1.370492 0.0055762 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 7 500 5 linear nonlinear 0.7315 0.4499842 0.4345209 0.0154634 0.5011860 0.4988208 1.373247 0.7670 0.8196 0.4449009 1.369545 0.0103801 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 8 500 5 linear nonlinear 0.6885 0.4450334 0.4345722 0.0104612 0.5002468 0.5009004 1.369128 0.8131 0.8469 0.4422738 1.370786 0.0077016 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 9 500 5 linear nonlinear 0.7025 0.4505258 0.4332923 0.0172336 0.4998549 0.4977071 1.388491 0.7677 0.7779 0.4494016 1.392270 0.0161094 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 10 500 5 linear nonlinear 0.7045 0.4803581 0.4350260 0.0453321 0.5007152 0.5004994 1.413894 0.6719 0.7711 0.4523531 1.408375 0.0173271 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 11 500 5 linear nonlinear 0.6960 0.4849641 0.4351553 0.0498088 0.5018875 0.5000687 1.381027 0.4894 0.8230 0.4449627 1.388530 0.0098074 26 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 12 500 5 linear nonlinear 0.7430 0.4446991 0.4339957 0.0107035 0.4991745 0.5000245 1.368082 0.8191 0.8369 0.4427240 1.368888 0.0087283 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 13 500 5 linear nonlinear 0.7180 0.4750358 0.4350971 0.0399387 0.5004399 0.5006546 1.397422 0.6275 0.7115 0.4607966 1.385394 0.0256995 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 14 500 5 linear nonlinear 0.7450 0.4482647 0.4334902 0.0147745 0.4984827 0.4991629 1.379169 0.7775 0.8718 0.4386937 1.365433 0.0052035 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 15 500 5 linear nonlinear 0.6885 0.4585448 0.4336523 0.0248925 0.4994206 0.4998486 1.401972 0.7226 0.8624 0.4400345 1.374854 0.0063821 19 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 16 500 5 linear nonlinear 0.7315 0.4569336 0.4329167 0.0240169 0.5000750 0.4979210 1.396758 0.7266 0.7516 0.4528489 1.387469 0.0199323 23 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 17 500 5 linear nonlinear 0.7530 0.4358150 0.4328235 0.0029915 0.4987848 0.4989231 1.363215 0.9080 0.9045 0.4359327 1.356549 0.0031092 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 18 500 5 linear nonlinear 0.6805 0.4488505 0.4353665 0.0134840 0.5016168 0.4991911 1.387288 0.8027 0.8178 0.4460377 1.385639 0.0106712 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 19 500 5 linear nonlinear 0.7355 0.4576950 0.4337273 0.0239678 0.4993589 0.4985463 1.382001 0.6837 0.8288 0.4409552 1.383944 0.0072279 19 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 20 500 5 linear nonlinear 0.7430 0.4416509 0.4333934 0.0082575 0.4986857 0.5004519 1.375059 0.8369 0.8553 0.4399270 1.377886 0.0065336 23 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 21 500 5 linear nonlinear 0.6620 0.4677853 0.4344562 0.0333291 0.5008391 0.5003493 1.388367 0.6670 0.8897 0.4384217 1.369405 0.0039655 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 22 500 5 linear nonlinear 0.7290 0.4494361 0.4325785 0.0168577 0.5000759 0.4967024 1.386733 0.7592 0.8728 0.4378091 1.382632 0.0052306 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 23 500 5 linear nonlinear 0.7535 0.4388260 0.4332958 0.0055302 0.4990446 0.5007135 1.365784 0.8961 0.8741 0.4383092 1.366312 0.0050134 18 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 24 500 5 linear nonlinear 0.7470 0.4417738 0.4343274 0.0074465 0.5005824 0.5000058 1.378283 0.8481 0.8484 0.4414823 1.376639 0.0071549 25 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 25 500 5 linear nonlinear 0.7370 0.4503425 0.4321157 0.0182268 0.4986998 0.4992784 1.369967 0.7593 0.7844 0.4469347 1.364714 0.0148189 22 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 26 500 5 linear nonlinear 0.7125 0.4658285 0.4347985 0.0310300 0.4995843 0.5008294 1.385581 0.6821 0.8029 0.4470476 1.372826 0.0122491 24 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 27 500 5 linear nonlinear 0.7250 0.4486014 0.4353729 0.0132284 0.5023647 0.4995561 1.382102 0.8066 0.8073 0.4476009 1.380709 0.0122280 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 28 500 5 linear nonlinear 0.6965 0.4519215 0.4345135 0.0174080 0.4983886 0.5011232 1.368224 0.7707 0.7986 0.4471055 1.366587 0.0125920 19 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 29 500 5 linear nonlinear 0.6965 0.4636100 0.4344848 0.0291252 0.5001010 0.4997000 1.373034 0.6901 0.8237 0.4442167 1.365255 0.0097319 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 30 500 5 linear nonlinear 0.7145 0.4434549 0.4332041 0.0102508 0.4976157 0.4995340 1.375232 0.8275 0.8322 0.4423239 1.372732 0.0091198 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 31 500 5 linear nonlinear 0.7415 0.4468601 0.4346814 0.0121787 0.5009485 0.4990330 1.377271 0.8192 0.8639 0.4404426 1.372664 0.0057612 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 32 500 5 linear nonlinear 0.7285 0.4526655 0.4336226 0.0190428 0.5006343 0.4990662 1.385777 0.7961 0.8189 0.4442726 1.384498 0.0106500 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 33 500 5 linear nonlinear 0.7280 0.4600353 0.4335860 0.0264492 0.4997082 0.4980328 1.389640 0.6841 0.6721 0.4650514 1.388122 0.0314654 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 34 500 5 linear nonlinear 0.7210 0.4578914 0.4341732 0.0237183 0.4998185 0.4989675 1.390689 0.7207 0.7483 0.4532775 1.393929 0.0191043 25 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 35 500 5 linear nonlinear 0.7275 0.4807933 0.4349355 0.0458578 0.5003912 0.5010102 1.399298 0.6206 0.7032 0.4606816 1.394391 0.0257461 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 36 500 5 linear nonlinear 0.7155 0.4595831 0.4343568 0.0252263 0.5007899 0.4997592 1.375617 0.7121 0.7834 0.4492238 1.375274 0.0148670 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 37 500 5 linear nonlinear 0.7440 0.4422355 0.4357799 0.0064556 0.4994606 0.5020033 1.377036 0.8762 0.9085 0.4385656 1.370874 0.0027857 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 38 500 5 linear nonlinear 0.7510 0.4358893 0.4331927 0.0026966 0.5004921 0.4982763 1.365787 0.9107 0.8942 0.4368843 1.366897 0.0036916 21 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 39 500 5 linear nonlinear 0.6420 0.4556631 0.4336821 0.0219810 0.4988399 0.5010004 1.388244 0.7293 0.8108 0.4452990 1.385525 0.0116169 25 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 40 500 5 linear nonlinear 0.7135 0.4606356 0.4330403 0.0275953 0.5000858 0.4975825 1.395081 0.6328 0.8307 0.4418125 1.399380 0.0087722 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 41 500 5 linear nonlinear 0.6995 0.4456316 0.4338266 0.0118050 0.5000804 0.5000257 1.383064 0.8112 0.8925 0.4378602 1.374810 0.0040336 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 42 500 5 linear nonlinear 0.6950 0.4575724 0.4344138 0.0231586 0.5000845 0.5004967 1.377986 0.7392 0.9012 0.4376752 1.375383 0.0032614 21 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 43 500 5 linear nonlinear 0.6520 0.4462166 0.4347425 0.0114741 0.5022210 0.4994072 1.389217 0.8114 0.8306 0.4442640 1.399125 0.0095215 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 44 500 5 linear nonlinear 0.7465 0.4499673 0.4345101 0.0154572 0.5009811 0.5000015 1.378453 0.7738 0.8242 0.4442494 1.380747 0.0097394 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 45 500 5 linear nonlinear 0.7315 0.4540072 0.4334532 0.0205539 0.4998499 0.4979048 1.373750 0.8217 0.8345 0.4413807 1.369768 0.0079274 21 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 46 500 5 linear nonlinear 0.7340 0.4358879 0.4336963 0.0021915 0.4987744 0.5001228 1.365856 0.9228 0.9230 0.4357201 1.366759 0.0020238 21 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 47 500 5 linear nonlinear 0.6095 0.4478909 0.4343195 0.0135715 0.5007256 0.5000183 1.415119 0.8034 0.8451 0.4420160 1.415239 0.0076966 21 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 48 500 5 linear nonlinear 0.7550 0.4673561 0.4322295 0.0351265 0.5000767 0.4985567 1.385164 0.6674 0.7796 0.4483102 1.382724 0.0160807 21 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 49 500 5 linear nonlinear 0.7480 0.4585954 0.4337708 0.0248246 0.4983829 0.4994059 1.400883 0.7516 0.8845 0.4379162 1.371802 0.0041454 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 50 500 5 linear nonlinear 0.6750 0.4615585 0.4349652 0.0265934 0.5005523 0.5010938 1.375117 0.6687 0.8350 0.4431111 1.372942 0.0081459 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 51 500 5 linear nonlinear 0.7380 0.4391393 0.4333583 0.0057810 0.4989806 0.4992403 1.358816 0.8663 0.8646 0.4391966 1.353685 0.0058383 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 52 500 5 linear nonlinear 0.7625 0.4530153 0.4361723 0.0168430 0.5013713 0.5016483 1.374639 0.7669 0.8026 0.4491069 1.378509 0.0129347 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 53 500 5 linear nonlinear 0.6920 0.4803221 0.4343677 0.0459545 0.4995542 0.5017517 1.426021 0.5984 0.7695 0.4515696 1.391890 0.0172020 19 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 54 500 5 linear nonlinear 0.6740 0.4612086 0.4357492 0.0254594 0.5008281 0.5009425 1.378804 0.6965 0.8278 0.4451463 1.370105 0.0093970 19 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 55 500 5 linear nonlinear 0.7070 0.4602548 0.4336804 0.0265744 0.4988286 0.5008494 1.396724 0.6979 0.7242 0.4562233 1.400607 0.0225429 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 56 500 5 linear nonlinear 0.7210 0.4778621 0.4347643 0.0430977 0.5014526 0.4984774 1.387111 0.6061 0.7428 0.4551357 1.388172 0.0203714 24 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 57 500 5 linear nonlinear 0.7465 0.4504689 0.4343142 0.0161548 0.5011205 0.4989928 1.375979 0.7643 0.8526 0.4410582 1.366336 0.0067440 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 58 500 5 linear nonlinear 0.7545 0.4614618 0.4348192 0.0266426 0.5001701 0.4998458 1.368635 0.6803 0.8553 0.4416298 1.368114 0.0068107 23 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 59 500 5 linear nonlinear 0.7485 0.4675428 0.4338063 0.0337365 0.5009257 0.4978266 1.380074 0.6392 0.8605 0.4400613 1.377895 0.0062550 20 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 60 500 5 linear nonlinear 0.7485 0.4433670 0.4327823 0.0105847 0.4995209 0.4993571 1.378259 0.8201 0.7817 0.4481654 1.389592 0.0153831 20 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 61 500 5 linear nonlinear 0.7095 0.4547299 0.4339135 0.0208164 0.4993688 0.4996465 1.372421 0.7204 0.8628 0.4401188 1.364893 0.0062053 21 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 62 500 5 linear nonlinear 0.6975 0.4538349 0.4333474 0.0204875 0.4983692 0.5011020 1.369534 0.7071 0.8297 0.4397956 1.367127 0.0064482 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 63 500 5 linear nonlinear 0.7795 0.4429713 0.4342078 0.0087634 0.5008023 0.4997317 1.375920 0.8531 0.8434 0.4419302 1.373442 0.0077223 20 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 64 500 5 linear nonlinear 0.7355 0.4744403 0.4346772 0.0397631 0.5002411 0.5012324 1.394154 0.6328 0.6804 0.4659056 1.400862 0.0312284 20 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 65 500 5 linear nonlinear 0.7455 0.4598367 0.4351787 0.0246579 0.4997124 0.5016802 1.384300 0.7260 0.8098 0.4467398 1.375617 0.0115611 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 66 500 5 linear nonlinear 0.7430 0.4704994 0.4368459 0.0336534 0.5020603 0.5020731 1.365346 0.7301 0.8502 0.4437058 1.363378 0.0068599 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 67 500 5 linear nonlinear 0.7250 0.4506192 0.4340867 0.0165325 0.4995806 0.5001846 1.376669 0.7840 0.8461 0.4414304 1.378585 0.0073437 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 68 500 5 linear nonlinear 0.6855 0.4415002 0.4338468 0.0076534 0.5004767 0.4999643 1.375683 0.8883 0.8812 0.4382808 1.390982 0.0044339 25 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 69 500 5 linear nonlinear 0.7130 0.4465646 0.4330117 0.0135529 0.4994848 0.5001328 1.387309 0.8057 0.9071 0.4357466 1.375856 0.0027349 21 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 70 500 5 linear nonlinear 0.7445 0.4534320 0.4347654 0.0186666 0.4999965 0.5013493 1.360539 0.8118 0.8831 0.4390635 1.352278 0.0042981 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 71 500 5 linear nonlinear 0.7415 0.4381567 0.4340699 0.0040869 0.4994720 0.4990600 1.371384 0.8558 0.8734 0.4387129 1.369526 0.0046431 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 72 500 5 linear nonlinear 0.6980 0.4899135 0.4343093 0.0556041 0.5013181 0.4975947 1.372055 0.5902 0.8131 0.4457200 1.364327 0.0114107 25 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 73 500 5 linear nonlinear 0.6925 0.4577502 0.4338230 0.0239273 0.4991321 0.4988790 1.418398 0.7079 0.8313 0.4430092 1.418478 0.0091863 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 74 500 5 linear nonlinear 0.7480 0.4721095 0.4353241 0.0367854 0.5004607 0.5008140 1.376606 0.6405 0.8237 0.4453435 1.382888 0.0100194 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 75 500 5 linear nonlinear 0.7145 0.4385091 0.4323392 0.0061699 0.4976946 0.4994537 1.367922 0.8724 0.8706 0.4377463 1.366199 0.0054072 18 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 76 500 5 linear nonlinear 0.7065 0.4550247 0.4344085 0.0206162 0.5008174 0.4997456 1.400638 0.7429 0.7709 0.4507838 1.400158 0.0163753 20 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 77 500 5 linear nonlinear 0.7345 0.4536559 0.4342529 0.0194030 0.5001438 0.4992473 1.376632 0.7519 0.7960 0.4466162 1.368407 0.0123633 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 78 500 5 linear nonlinear 0.7170 0.4699365 0.4335975 0.0363390 0.5004226 0.4989901 1.382653 0.6966 0.7586 0.4519040 1.368531 0.0183065 20 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 79 500 5 linear nonlinear 0.7445 0.4413744 0.4335022 0.0078722 0.5001969 0.4987648 1.368756 0.8452 0.8291 0.4428693 1.371941 0.0093671 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 80 500 5 linear nonlinear 0.7540 0.4755502 0.4339949 0.0415554 0.5000404 0.4994547 1.384836 0.6239 0.7093 0.4605924 1.385962 0.0265975 25 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 81 500 5 linear nonlinear 0.7080 0.4868083 0.4347004 0.0521078 0.5007375 0.4997454 1.390633 0.4943 0.8524 0.4417209 1.382714 0.0070204 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 82 500 5 linear nonlinear 0.6970 0.4590819 0.4336493 0.0254326 0.5002614 0.4975459 1.373856 0.6944 0.8993 0.4368364 1.370829 0.0031871 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 83 500 5 linear nonlinear 0.7400 0.4460556 0.4346729 0.0113828 0.5010206 0.4990307 1.373672 0.8170 0.8098 0.4459358 1.375222 0.0112630 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 84 500 5 linear nonlinear 0.6880 0.4563413 0.4336753 0.0226660 0.4986666 0.4998740 1.378488 0.7719 0.8388 0.4420144 1.391771 0.0083391 20 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 85 500 5 linear nonlinear 0.7390 0.4486041 0.4373131 0.0112910 0.5013841 0.5028470 1.386971 0.8158 0.8009 0.4497134 1.377538 0.0124003 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 86 500 5 linear nonlinear 0.7465 0.4397406 0.4344292 0.0053114 0.5003907 0.4997138 1.387237 0.8577 0.8833 0.4384098 1.395448 0.0039806 21 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 87 500 5 linear nonlinear 0.7225 0.4553654 0.4336978 0.0216676 0.4998128 0.4995604 1.377322 0.7402 0.7561 0.4526899 1.368691 0.0189921 21 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 88 500 5 linear nonlinear 0.7110 0.4413855 0.4331991 0.0081864 0.4995537 0.4982736 1.371015 0.8423 0.8221 0.4432445 1.378952 0.0100453 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 89 500 5 linear nonlinear 0.6925 0.4404763 0.4333662 0.0071101 0.5001094 0.4975517 1.389298 0.8624 0.8743 0.4383574 1.406940 0.0049912 22 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 90 500 5 linear nonlinear 0.6955 0.4651827 0.4328940 0.0322886 0.4997266 0.4988412 1.397457 0.6622 0.7942 0.4470974 1.390131 0.0142034 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 91 500 5 linear nonlinear 0.7370 0.4518156 0.4328066 0.0190089 0.4986092 0.5004586 1.376671 0.7973 0.8319 0.4422132 1.388306 0.0094065 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 92 500 5 linear nonlinear 0.6760 0.4688248 0.4340452 0.0347795 0.5002605 0.5010807 1.405029 0.7159 0.8398 0.4423997 1.373715 0.0083545 22 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 93 500 5 linear nonlinear 0.7070 0.4457424 0.4362843 0.0094581 0.5002267 0.5018751 1.386168 0.8196 0.8509 0.4428702 1.400565 0.0065859 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 94 500 5 linear nonlinear 0.7315 0.4499416 0.4324419 0.0174997 0.4991252 0.4973660 1.382131 0.7624 0.7847 0.4468000 1.381568 0.0143580 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 95 500 5 linear nonlinear 0.7520 0.4496792 0.4332595 0.0164197 0.4983815 0.4992727 1.384707 0.7738 0.7903 0.4463760 1.372616 0.0131165 19 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 96 500 5 linear nonlinear 0.7475 0.4473920 0.4327190 0.0146730 0.4988620 0.4997532 1.370713 0.8533 0.9413 0.4340018 1.365561 0.0012827 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 97 500 5 linear nonlinear 0.7370 0.5003801 0.4326402 0.0677399 0.5003801 0.4983052 1.375592 0.4933 0.7254 0.4561279 1.373275 0.0234877 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 98 500 5 linear nonlinear 0.6430 0.4525704 0.4346231 0.0179472 0.5005079 0.5005104 1.396147 0.7622 0.7693 0.4510926 1.390944 0.0164694 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 99 500 5 linear nonlinear 0.7400 0.4712539 0.4332373 0.0380167 0.4996832 0.4989672 1.400305 0.6425 0.8059 0.4453905 1.389921 0.0121532 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
13 100 500 5 linear nonlinear 0.7355 0.4441196 0.4364573 0.0076623 0.5011143 0.5025656 1.380961 0.8270 0.9098 0.4389734 1.390219 0.0025162 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
14 1 1000 5 linear nonlinear 0.7390 0.4417917 0.4355980 0.0061937 0.5010232 0.5019665 1.365546 0.8677 0.8846 0.4400351 1.365584 0.0044371 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 2 1000 5 linear nonlinear 0.7235 0.4405943 0.4350173 0.0055769 0.4999986 0.5010611 1.369319 0.8708 0.8823 0.4397429 1.370101 0.0047255 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 3 1000 5 linear nonlinear 0.7610 0.4456561 0.4353876 0.0102685 0.5010727 0.5012773 1.376153 0.8186 0.8148 0.4460984 1.372038 0.0107108 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 4 1000 5 linear nonlinear 0.7710 0.4395210 0.4350528 0.0044682 0.5011542 0.5007152 1.366194 0.8860 0.8865 0.4394202 1.362134 0.0043674 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 5 1000 5 linear nonlinear 0.7545 0.4507518 0.4341159 0.0166359 0.5003436 0.5000200 1.359754 0.7878 0.9122 0.4367546 1.351470 0.0026387 20 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 6 1000 5 linear nonlinear 0.7510 0.4561466 0.4325612 0.0235855 0.4997217 0.4967190 1.373498 0.7081 0.8520 0.4382332 1.366473 0.0056720 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 7 1000 5 linear nonlinear 0.6700 0.4484898 0.4345034 0.0139864 0.5003070 0.5016012 1.380664 0.7946 0.9317 0.4361874 1.371647 0.0016840 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 8 1000 5 linear nonlinear 0.7280 0.4374537 0.4334634 0.0039903 0.4985621 0.4981317 1.363212 0.8884 0.8827 0.4379779 1.362161 0.0045145 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 9 1000 5 linear nonlinear 0.6405 0.4602276 0.4348901 0.0253375 0.5009010 0.4998135 1.382223 0.6950 0.8587 0.4405484 1.371041 0.0056583 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 10 1000 5 linear nonlinear 0.7425 0.4379648 0.4342234 0.0037414 0.4993784 0.4991363 1.355544 0.8938 0.9121 0.4364741 1.349564 0.0022507 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 11 1000 5 linear nonlinear 0.7615 0.4410479 0.4339688 0.0070791 0.4980150 0.4990731 1.364329 0.8390 0.9242 0.4357066 1.360934 0.0017378 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 12 1000 5 linear nonlinear 0.7490 0.4441786 0.4345174 0.0096612 0.4999910 0.4998786 1.375307 0.8260 0.8121 0.4454702 1.369044 0.0109528 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 13 1000 5 linear nonlinear 0.7205 0.4383962 0.4348441 0.0035521 0.5018808 0.4995135 1.366699 0.9145 0.8414 0.4422842 1.361909 0.0074400 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 14 1000 5 linear nonlinear 0.7255 0.4619902 0.4333399 0.0286503 0.4996670 0.4989918 1.379760 0.6706 0.9300 0.4348257 1.367624 0.0014859 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 15 1000 5 linear nonlinear 0.7450 0.4420086 0.4331612 0.0088474 0.4986404 0.4985096 1.357701 0.8193 0.9207 0.4352768 1.355530 0.0021156 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 16 1000 5 linear nonlinear 0.7500 0.4400035 0.4352225 0.0047810 0.5002320 0.4996728 1.367107 0.8787 0.8837 0.4396983 1.363742 0.0044757 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 17 1000 5 linear nonlinear 0.7505 0.4409611 0.4332078 0.0077533 0.5000007 0.4985998 1.367019 0.8510 0.8787 0.4380791 1.361998 0.0048713 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 18 1000 5 linear nonlinear 0.7305 0.4946188 0.4346082 0.0600106 0.4994904 0.5000798 1.365211 0.5422 0.8487 0.4418586 1.360663 0.0072504 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 19 1000 5 linear nonlinear 0.7595 0.4567025 0.4352888 0.0214137 0.5006547 0.5013519 1.374524 0.7277 0.7568 0.4535334 1.373414 0.0182446 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 20 1000 5 linear nonlinear 0.7455 0.4460695 0.4346733 0.0113961 0.5014684 0.4991047 1.373292 0.8440 0.8581 0.4411921 1.378592 0.0065188 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 21 1000 5 linear nonlinear 0.7650 0.4455930 0.4338366 0.0117565 0.4997455 0.4988496 1.363315 0.7749 0.9277 0.4355899 1.361251 0.0017533 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 22 1000 5 linear nonlinear 0.7335 0.4335303 0.4327229 0.0008074 0.5004088 0.4983486 1.372058 0.9504 0.9600 0.4332198 1.369722 0.0004969 31 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 23 1000 5 linear nonlinear 0.7280 0.4376485 0.4352131 0.0024354 0.5005813 0.5012341 1.369133 0.9143 0.9231 0.4370137 1.368910 0.0018006 33 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 24 1000 5 linear nonlinear 0.7590 0.4640962 0.4334777 0.0306186 0.5002314 0.4992285 1.368872 0.7644 0.8603 0.4396072 1.365022 0.0061296 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 25 1000 5 linear nonlinear 0.7655 0.4403762 0.4333588 0.0070175 0.4995949 0.4981345 1.361125 0.8244 0.9495 0.4340778 1.363513 0.0007191 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 26 1000 5 linear nonlinear 0.7510 0.4371944 0.4344719 0.0027225 0.4990803 0.4993878 1.369017 0.9267 0.8809 0.4389553 1.362986 0.0044835 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 27 1000 5 linear nonlinear 0.7670 0.4429250 0.4350172 0.0079078 0.5002822 0.5020481 1.369052 0.8678 0.8834 0.4397339 1.368560 0.0047168 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 28 1000 5 linear nonlinear 0.7485 0.4573643 0.4335445 0.0238198 0.5009399 0.4984684 1.358320 0.7008 0.8666 0.4392558 1.355064 0.0057113 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 29 1000 5 linear nonlinear 0.7265 0.4733682 0.4343940 0.0389743 0.5004274 0.5007396 1.371159 0.7084 0.7730 0.4514193 1.364388 0.0170253 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 30 1000 5 linear nonlinear 0.7180 0.4440860 0.4350011 0.0090850 0.5011817 0.5002730 1.371362 0.8327 0.8515 0.4422163 1.366590 0.0072152 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 31 1000 5 linear nonlinear 0.7015 0.4376365 0.4350154 0.0026211 0.5013969 0.5012225 1.378776 0.9171 0.8836 0.4393652 1.374759 0.0043498 27 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 32 1000 5 linear nonlinear 0.7375 0.4640719 0.4366595 0.0274124 0.5007968 0.5016502 1.381384 0.7213 0.8073 0.4484732 1.362837 0.0118137 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 33 1000 5 linear nonlinear 0.7660 0.4387397 0.4336455 0.0050942 0.4997920 0.5002678 1.362602 0.8788 0.8527 0.4406008 1.358518 0.0069553 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 34 1000 5 linear nonlinear 0.7685 0.4398295 0.4349101 0.0049194 0.5001880 0.4999386 1.371043 0.8772 0.8883 0.4389967 1.367170 0.0040866 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 35 1000 5 linear nonlinear 0.7430 0.4399415 0.4345450 0.0053965 0.5002145 0.5005405 1.379559 0.8757 0.9213 0.4365073 1.368146 0.0019623 20 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 36 1000 5 linear nonlinear 0.7835 0.4455464 0.4340275 0.0115189 0.5009687 0.4978524 1.370239 0.8110 0.8324 0.4427991 1.374909 0.0087715 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 37 1000 5 linear nonlinear 0.7550 0.4459794 0.4349993 0.0109801 0.4995209 0.4999875 1.361846 0.7935 0.9009 0.4381901 1.361699 0.0031908 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 38 1000 5 linear nonlinear 0.7210 0.4378808 0.4348870 0.0029938 0.4994343 0.5009783 1.374208 0.9091 0.8949 0.4382776 1.373076 0.0033906 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 39 1000 5 linear nonlinear 0.7245 0.4375104 0.4333326 0.0041779 0.4986880 0.4989406 1.378907 0.9097 0.9110 0.4360391 1.373125 0.0027066 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 40 1000 5 linear nonlinear 0.7175 0.4538800 0.4348343 0.0190458 0.5000279 0.5006724 1.371634 0.7386 0.8319 0.4437952 1.370481 0.0089609 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 41 1000 5 linear nonlinear 0.7410 0.4768174 0.4340944 0.0427229 0.5006550 0.4987890 1.370696 0.6010 0.7866 0.4474780 1.363552 0.0133836 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 42 1000 5 linear nonlinear 0.7360 0.4460568 0.4334042 0.0126526 0.4977822 0.5001647 1.368031 0.7741 0.9139 0.4359017 1.370559 0.0024975 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 43 1000 5 linear nonlinear 0.7475 0.4507698 0.4337233 0.0170464 0.5000451 0.4994656 1.370971 0.8606 0.9263 0.4354982 1.360087 0.0017748 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 44 1000 5 linear nonlinear 0.7615 0.4350974 0.4335615 0.0015358 0.4995776 0.4984981 1.367106 0.9299 0.8560 0.4388805 1.363435 0.0053189 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 45 1000 5 linear nonlinear 0.7265 0.4516122 0.4347176 0.0168946 0.5001236 0.5001626 1.377160 0.7553 0.8513 0.4415846 1.378891 0.0068670 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 46 1000 5 linear nonlinear 0.7575 0.4434977 0.4345404 0.0089573 0.4987532 0.4998568 1.370612 0.8315 0.8274 0.4439973 1.372554 0.0094569 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 47 1000 5 linear nonlinear 0.7135 0.4456050 0.4336539 0.0119510 0.4993212 0.4988175 1.377179 0.7970 0.8424 0.4412086 1.370856 0.0075546 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 48 1000 5 linear nonlinear 0.7235 0.4463001 0.4324993 0.0138008 0.4995597 0.4980170 1.369360 0.7870 0.8636 0.4386380 1.366039 0.0061388 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 49 1000 5 linear nonlinear 0.7535 0.4692723 0.4362216 0.0330508 0.5023586 0.5020846 1.373629 0.5519 0.9040 0.4392835 1.373414 0.0030619 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 50 1000 5 linear nonlinear 0.7055 0.4391652 0.4341498 0.0050154 0.5004939 0.5000035 1.360759 0.8641 0.9212 0.4360768 1.361869 0.0019270 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 51 1000 5 linear nonlinear 0.7835 0.4419827 0.4350035 0.0069792 0.4998625 0.5018477 1.367851 0.8349 0.9290 0.4368011 1.363126 0.0017976 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 52 1000 5 linear nonlinear 0.7600 0.4395575 0.4345056 0.0050519 0.5001308 0.5003494 1.358431 0.8795 0.8708 0.4398589 1.355751 0.0053533 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 53 1000 5 linear nonlinear 0.7265 0.4516634 0.4326725 0.0189909 0.4989807 0.4987883 1.367673 0.7245 0.8548 0.4400000 1.365993 0.0073276 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 54 1000 5 linear nonlinear 0.7300 0.4411336 0.4335390 0.0075946 0.5011352 0.4983997 1.371137 0.8354 0.9142 0.4359628 1.369156 0.0024238 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 55 1000 5 linear nonlinear 0.7350 0.4373930 0.4334770 0.0039159 0.4997353 0.4999483 1.365067 0.8874 0.9013 0.4365543 1.366798 0.0030773 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 56 1000 5 linear nonlinear 0.7585 0.4413470 0.4359411 0.0054059 0.5007505 0.5022196 1.363393 0.8812 0.9232 0.4378857 1.358414 0.0019447 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 57 1000 5 linear nonlinear 0.7230 0.4462601 0.4337088 0.0125513 0.5003417 0.4988788 1.386743 0.7914 0.8889 0.4373924 1.372367 0.0036836 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 58 1000 5 linear nonlinear 0.7425 0.4411824 0.4339299 0.0072525 0.4989912 0.5009808 1.367707 0.8242 0.9034 0.4367047 1.365142 0.0027748 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 59 1000 5 linear nonlinear 0.7490 0.4419978 0.4333950 0.0086028 0.5001610 0.4981881 1.362176 0.8244 0.9220 0.4354283 1.362827 0.0020333 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 60 1000 5 linear nonlinear 0.7150 0.4433910 0.4349694 0.0084216 0.4999854 0.5008765 1.377035 0.8370 0.8204 0.4449860 1.377056 0.0100165 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 61 1000 5 linear nonlinear 0.7385 0.4406133 0.4334106 0.0072027 0.4990749 0.4995314 1.365430 0.8651 0.8551 0.4403016 1.360983 0.0068910 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 62 1000 5 linear nonlinear 0.7485 0.4435417 0.4346207 0.0089210 0.5008317 0.5014818 1.362954 0.8308 0.8557 0.4411988 1.360934 0.0065782 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 63 1000 5 linear nonlinear 0.7050 0.4398688 0.4352572 0.0046116 0.4996606 0.5021915 1.356021 0.8819 0.8709 0.4404970 1.356396 0.0052398 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 64 1000 5 linear nonlinear 0.7760 0.4382397 0.4325981 0.0056416 0.4984325 0.4975753 1.370147 0.8635 0.9074 0.4353111 1.376676 0.0027129 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 65 1000 5 linear nonlinear 0.7475 0.4419867 0.4339405 0.0080462 0.4985660 0.4997815 1.364180 0.8422 0.8391 0.4421211 1.363724 0.0081805 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 66 1000 5 linear nonlinear 0.7220 0.4442730 0.4345039 0.0097692 0.4991448 0.5008163 1.375510 0.8464 0.8445 0.4419164 1.361884 0.0074125 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 67 1000 5 linear nonlinear 0.7220 0.4446000 0.4339095 0.0106905 0.5006583 0.4985475 1.367375 0.8147 0.8091 0.4448870 1.370839 0.0109775 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 68 1000 5 linear nonlinear 0.7445 0.4369005 0.4335910 0.0033095 0.4983579 0.4996759 1.359411 0.9151 0.9312 0.4351858 1.361463 0.0015948 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 69 1000 5 linear nonlinear 0.7415 0.4645796 0.4328519 0.0317277 0.4995190 0.4990861 1.364862 0.7408 0.8573 0.4393496 1.363277 0.0064976 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 70 1000 5 linear nonlinear 0.7465 0.4491748 0.4361912 0.0129837 0.5007723 0.5019622 1.361016 0.7936 0.8917 0.4397945 1.358992 0.0036033 25 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 71 1000 5 linear nonlinear 0.7695 0.4398181 0.4340289 0.0057892 0.5010376 0.4984287 1.366888 0.8512 0.8708 0.4384953 1.366839 0.0044664 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 72 1000 5 linear nonlinear 0.7460 0.4523120 0.4359628 0.0163492 0.5008278 0.5029830 1.363229 0.7566 0.8880 0.4395325 1.359760 0.0035697 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 73 1000 5 linear nonlinear 0.7755 0.4449060 0.4368751 0.0080308 0.5010542 0.5033962 1.355249 0.8994 0.9247 0.4384654 1.354592 0.0015903 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 74 1000 5 linear nonlinear 0.7275 0.4569704 0.4347340 0.0222364 0.4990212 0.5005374 1.361787 0.6909 0.8472 0.4416438 1.357148 0.0069098 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 75 1000 5 linear nonlinear 0.7385 0.4408896 0.4331185 0.0077711 0.4988872 0.5003878 1.369674 0.8478 0.8722 0.4385115 1.363549 0.0053930 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 76 1000 5 linear nonlinear 0.7280 0.4504697 0.4344254 0.0160443 0.4996730 0.5002483 1.369837 0.7666 0.8728 0.4398011 1.366759 0.0053757 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 77 1000 5 linear nonlinear 0.7550 0.4397891 0.4321800 0.0076091 0.4984392 0.4994858 1.367471 0.8438 0.8996 0.4355254 1.362298 0.0033454 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 78 1000 5 linear nonlinear 0.7595 0.4537769 0.4318552 0.0219216 0.4990565 0.4971092 1.364445 0.7185 0.8627 0.4379967 1.366605 0.0061414 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 79 1000 5 linear nonlinear 0.6470 0.4438518 0.4356401 0.0082117 0.5025543 0.5003865 1.373682 0.8456 0.8759 0.4408615 1.369899 0.0052214 29 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 80 1000 5 linear nonlinear 0.7345 0.4377431 0.4330699 0.0046733 0.4990229 0.4983908 1.365922 0.8947 0.8906 0.4371897 1.362744 0.0041199 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 81 1000 5 linear nonlinear 0.7475 0.4369236 0.4338323 0.0030913 0.4987675 0.4987040 1.365369 0.8974 0.8815 0.4378123 1.365655 0.0039799 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 82 1000 5 linear nonlinear 0.7320 0.4407905 0.4354893 0.0053011 0.4995856 0.5018489 1.381405 0.8970 0.9077 0.4381371 1.374208 0.0026478 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 83 1000 5 linear nonlinear 0.7265 0.4409343 0.4352163 0.0057180 0.5005217 0.5012998 1.360121 0.8822 0.8927 0.4389087 1.360771 0.0036924 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 84 1000 5 linear nonlinear 0.7380 0.4390857 0.4329300 0.0061556 0.4994049 0.4986839 1.370052 0.8656 0.8260 0.4426484 1.369549 0.0097184 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 85 1000 5 linear nonlinear 0.7505 0.4432224 0.4355088 0.0077136 0.5007141 0.5023246 1.361342 0.8436 0.8916 0.4392116 1.354799 0.0037028 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 86 1000 5 linear nonlinear 0.7685 0.4371592 0.4349491 0.0022101 0.5007145 0.5001281 1.367506 0.9118 0.9270 0.4365614 1.364537 0.0016123 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 87 1000 5 linear nonlinear 0.7150 0.4452307 0.4334738 0.0117568 0.4993688 0.5006914 1.366349 0.8032 0.8725 0.4389500 1.360912 0.0054762 26 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 88 1000 5 linear nonlinear 0.7465 0.4423040 0.4345574 0.0077466 0.4996307 0.5010111 1.364102 0.8434 0.8763 0.4396927 1.361760 0.0051353 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 89 1000 5 linear nonlinear 0.7580 0.4442636 0.4343679 0.0098957 0.5000923 0.5003296 1.366187 0.8164 0.8724 0.4393934 1.366905 0.0050256 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 90 1000 5 linear nonlinear 0.7565 0.4383094 0.4341702 0.0041392 0.4985494 0.5005806 1.365399 0.8895 0.8934 0.4379940 1.361808 0.0038238 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 91 1000 5 linear nonlinear 0.7480 0.4520178 0.4364947 0.0155230 0.5021738 0.5020014 1.373235 0.7690 0.8575 0.4431478 1.366156 0.0066530 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 92 1000 5 linear nonlinear 0.7090 0.4442568 0.4364339 0.0078230 0.5016408 0.5023938 1.377665 0.8450 0.8931 0.4395227 1.364816 0.0030888 21 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 93 1000 5 linear nonlinear 0.7280 0.4345914 0.4340572 0.0005342 0.4995725 0.4992168 1.364515 0.9594 0.9799 0.4341881 1.360267 0.0001309 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 94 1000 5 linear nonlinear 0.7445 0.4573036 0.4347333 0.0225703 0.5009859 0.5014077 1.369345 0.7256 0.7734 0.4512240 1.369874 0.0164906 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 95 1000 5 linear nonlinear 0.7265 0.4450072 0.4355170 0.0094903 0.5006774 0.5009338 1.363451 0.8131 0.8708 0.4408047 1.365372 0.0052877 26 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 96 1000 5 linear nonlinear 0.7355 0.4401998 0.4344314 0.0057684 0.4997229 0.4996817 1.370096 0.8676 0.8870 0.4386716 1.372113 0.0042402 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 97 1000 5 linear nonlinear 0.7345 0.4430369 0.4334399 0.0095970 0.4999369 0.4987815 1.369454 0.8270 0.8717 0.4386976 1.363842 0.0052577 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 98 1000 5 linear nonlinear 0.7560 0.4541985 0.4353504 0.0188481 0.4997979 0.5009434 1.363688 0.7409 0.8477 0.4426302 1.361622 0.0072797 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 99 1000 5 linear nonlinear 0.6690 0.4554376 0.4376217 0.0178159 0.5025730 0.5028519 1.373096 0.7583 0.8994 0.4408344 1.354016 0.0032127 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
14 100 1000 5 linear nonlinear 0.7575 0.4404813 0.4329933 0.0074879 0.4977101 0.4998209 1.367740 0.8502 0.8442 0.4413277 1.368488 0.0083344 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
15 1 2000 5 linear nonlinear 0.7600 0.4349620 0.4330520 0.0019101 0.4996853 0.4978445 1.364298 0.9283 0.9279 0.4347066 1.361198 0.0016547 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 2 2000 5 linear nonlinear 0.7210 0.4402325 0.4367270 0.0035056 0.5012301 0.5030397 1.353238 0.9034 0.9299 0.4383573 1.347882 0.0016304 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 3 2000 5 linear nonlinear 0.7560 0.4542671 0.4351191 0.0191480 0.5006463 0.5003805 1.362422 0.8013 0.9462 0.4360344 1.356633 0.0009154 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 4 2000 5 linear nonlinear 0.7465 0.4472775 0.4342740 0.0130035 0.4990928 0.5004729 1.361454 0.7893 0.8989 0.4376601 1.357996 0.0033861 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 5 2000 5 linear nonlinear 0.7690 0.4433871 0.4356523 0.0077348 0.4987209 0.5024557 1.350779 0.8345 0.9341 0.4369686 1.345678 0.0013164 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 6 2000 5 linear nonlinear 0.7490 0.4383162 0.4348901 0.0034261 0.5010486 0.4995697 1.370273 0.8939 0.9064 0.4376065 1.365677 0.0027164 37 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 7 2000 5 linear nonlinear 0.7375 0.4365330 0.4341378 0.0023952 0.4988654 0.5008871 1.362826 0.9177 0.9451 0.4351494 1.361116 0.0010117 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 8 2000 5 linear nonlinear 0.7705 0.4373888 0.4335239 0.0038649 0.4995494 0.4993000 1.367390 0.8905 0.9202 0.4357581 1.363816 0.0022342 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 9 2000 5 linear nonlinear 0.7555 0.4395142 0.4341437 0.0053705 0.4999236 0.4991419 1.369582 0.9052 0.9298 0.4357294 1.366329 0.0015857 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 10 2000 5 linear nonlinear 0.7345 0.4446294 0.4346408 0.0099886 0.4995188 0.5020704 1.364134 0.8055 0.8842 0.4378186 1.359685 0.0031778 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 11 2000 5 linear nonlinear 0.7240 0.4505709 0.4345157 0.0160553 0.5005832 0.4997333 1.358849 0.7900 0.8826 0.4389176 1.353241 0.0044019 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 12 2000 5 linear nonlinear 0.7380 0.4410603 0.4341670 0.0068933 0.5010270 0.4991623 1.357844 0.8435 0.8989 0.4374325 1.356537 0.0032655 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 13 2000 5 linear nonlinear 0.7460 0.4372824 0.4346184 0.0026639 0.5012504 0.4994418 1.367795 0.9001 0.9407 0.4357941 1.363726 0.0011757 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 14 2000 5 linear nonlinear 0.7465 0.4526694 0.4332899 0.0193795 0.4996463 0.4996161 1.369007 0.6947 0.9209 0.4352422 1.365597 0.0019523 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 15 2000 5 linear nonlinear 0.7500 0.4472283 0.4336775 0.0135507 0.5007951 0.4993329 1.362883 0.7929 0.8522 0.4408201 1.362446 0.0071426 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 16 2000 5 linear nonlinear 0.7435 0.4386188 0.4340001 0.0046187 0.4997943 0.5008801 1.361641 0.8804 0.9284 0.4357922 1.353177 0.0017922 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 17 2000 5 linear nonlinear 0.7575 0.4520980 0.4343629 0.0177351 0.4997706 0.4997187 1.359426 0.8470 0.8871 0.4384400 1.355651 0.0040771 37 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 18 2000 5 linear nonlinear 0.7550 0.4393374 0.4336993 0.0056381 0.4989136 0.4996947 1.366432 0.8752 0.8787 0.4382604 1.364979 0.0045611 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 19 2000 5 linear nonlinear 0.7480 0.4411243 0.4346975 0.0064268 0.4987965 0.5001144 1.363502 0.8714 0.9106 0.4369726 1.360819 0.0022752 35 0.0 0.6 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 20 2000 5 linear nonlinear 0.7415 0.4361062 0.4334886 0.0026176 0.4975497 0.5009224 1.364456 0.9137 0.9007 0.4367250 1.362625 0.0032364 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 21 2000 5 linear nonlinear 0.7675 0.4641453 0.4341216 0.0300237 0.4989901 0.4995872 1.372280 0.7043 0.8724 0.4388570 1.362900 0.0047353 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 22 2000 5 linear nonlinear 0.7510 0.4400452 0.4339649 0.0060804 0.4999034 0.4993716 1.372382 0.8602 0.8710 0.4391592 1.366520 0.0051943 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 23 2000 5 linear nonlinear 0.7545 0.4435621 0.4333800 0.0101821 0.4991933 0.4987079 1.367530 0.8068 0.8862 0.4372534 1.365294 0.0038734 38 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 24 2000 5 linear nonlinear 0.7590 0.4440054 0.4337435 0.0102619 0.5000433 0.5005172 1.357015 0.7781 0.9123 0.4363971 1.354489 0.0026537 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 25 2000 5 linear nonlinear 0.7660 0.4400820 0.4330469 0.0070350 0.5003157 0.4982017 1.360364 0.8540 0.9001 0.4363893 1.357291 0.0033424 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 26 2000 5 linear nonlinear 0.7415 0.4412696 0.4343761 0.0068935 0.4998134 0.5009031 1.361601 0.8501 0.9073 0.4371230 1.359341 0.0027469 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 27 2000 5 linear nonlinear 0.7460 0.4460713 0.4346764 0.0113949 0.4990198 0.5016588 1.361937 0.8041 0.8698 0.4399987 1.359056 0.0053223 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 28 2000 5 linear nonlinear 0.7545 0.4384841 0.4341468 0.0043373 0.4991494 0.5006113 1.366879 0.8821 0.8976 0.4373252 1.363980 0.0031784 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 29 2000 5 linear nonlinear 0.7420 0.4382479 0.4337048 0.0045431 0.5002202 0.4976812 1.362642 0.8826 0.8617 0.4398881 1.359712 0.0061833 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 30 2000 5 linear nonlinear 0.7235 0.4408582 0.4323832 0.0084751 0.4985441 0.4988849 1.353664 0.8365 0.8926 0.4362359 1.351018 0.0038528 33 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 31 2000 5 linear nonlinear 0.7515 0.4384017 0.4336144 0.0047874 0.4993189 0.5004730 1.352394 0.8656 0.9265 0.4352998 1.347661 0.0016855 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 32 2000 5 linear nonlinear 0.7580 0.4425225 0.4349614 0.0075611 0.4998511 0.5028425 1.361180 0.8365 0.8928 0.4389244 1.361822 0.0039630 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 33 2000 5 linear nonlinear 0.7525 0.4370213 0.4331385 0.0038827 0.4992042 0.4987505 1.369730 0.8882 0.9143 0.4356153 1.366782 0.0024768 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 34 2000 5 linear nonlinear 0.7425 0.4460829 0.4325288 0.0135540 0.4993653 0.4976258 1.360310 0.8764 0.9019 0.4356900 1.355880 0.0031612 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 35 2000 5 linear nonlinear 0.7395 0.4440218 0.4350615 0.0089603 0.5000616 0.5027562 1.358449 0.8014 0.9447 0.4361195 1.358189 0.0010579 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 36 2000 5 linear nonlinear 0.7260 0.4367210 0.4351561 0.0015650 0.4991990 0.5023564 1.362527 0.9276 0.9160 0.4373022 1.356447 0.0021461 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 37 2000 5 linear nonlinear 0.7810 0.4378531 0.4345253 0.0033277 0.5010484 0.4991316 1.359860 0.9384 0.9361 0.4358355 1.361311 0.0013102 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 38 2000 5 linear nonlinear 0.7720 0.4341372 0.4336437 0.0004936 0.5004162 0.4976737 1.365292 0.9599 0.9556 0.4342586 1.366507 0.0006149 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 39 2000 5 linear nonlinear 0.7370 0.4518901 0.4325198 0.0193703 0.4992032 0.4991768 1.371079 0.7514 0.8310 0.4415192 1.360760 0.0089994 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 40 2000 5 linear nonlinear 0.7155 0.4356030 0.4314908 0.0041122 0.4978240 0.4975446 1.359899 0.8891 0.9131 0.4339082 1.357761 0.0024174 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 41 2000 5 linear nonlinear 0.7365 0.4384623 0.4344083 0.0040539 0.4988900 0.5007147 1.365269 0.8885 0.9106 0.4370948 1.365055 0.0026864 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 42 2000 5 linear nonlinear 0.7605 0.4397163 0.4343369 0.0053793 0.5005012 0.4995787 1.360005 0.8716 0.8730 0.4394433 1.358513 0.0051063 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 43 2000 5 linear nonlinear 0.7380 0.4382143 0.4334476 0.0047667 0.4991158 0.4999768 1.357159 0.8801 0.9163 0.4357155 1.353195 0.0022679 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 44 2000 5 linear nonlinear 0.7510 0.4413488 0.4327804 0.0085684 0.4992241 0.4977090 1.359547 0.8342 0.8853 0.4369728 1.358615 0.0041923 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 45 2000 5 linear nonlinear 0.7350 0.4416113 0.4343210 0.0072903 0.4997954 0.4997789 1.353733 0.8632 0.8791 0.4389889 1.352253 0.0046679 38 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 46 2000 5 linear nonlinear 0.7370 0.4560937 0.4340284 0.0220653 0.4995557 0.5008425 1.354375 0.6923 0.9149 0.4364111 1.353007 0.0023827 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 47 2000 5 linear nonlinear 0.7585 0.4415558 0.4348968 0.0066590 0.4999415 0.4997861 1.361611 0.8905 0.9228 0.4367519 1.355868 0.0018551 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 48 2000 5 linear nonlinear 0.7650 0.4449040 0.4338884 0.0110156 0.5000333 0.4996765 1.366653 0.8061 0.8958 0.4374746 1.364548 0.0035862 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 49 2000 5 linear nonlinear 0.7470 0.4363027 0.4341230 0.0021797 0.5005367 0.4980069 1.362428 0.9193 0.9137 0.4364281 1.357516 0.0023052 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 50 2000 5 linear nonlinear 0.7340 0.4393189 0.4342717 0.0050472 0.5003353 0.4987182 1.361923 0.8697 0.9244 0.4361789 1.357981 0.0019072 40 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 51 2000 5 linear nonlinear 0.7630 0.4353286 0.4334416 0.0018870 0.4999155 0.4974610 1.363741 0.9230 0.9526 0.4341125 1.360298 0.0006709 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 52 2000 5 linear nonlinear 0.7435 0.4378727 0.4338150 0.0040577 0.5001121 0.4993691 1.361617 0.8878 0.9363 0.4350933 1.357134 0.0012783 36 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 53 2000 5 linear nonlinear 0.7705 0.4354823 0.4336199 0.0018624 0.4995495 0.4994080 1.361698 0.9455 0.9304 0.4352122 1.359891 0.0015923 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 54 2000 5 linear nonlinear 0.7605 0.4400686 0.4343175 0.0057511 0.5004159 0.5006938 1.367911 0.8727 0.9111 0.4371158 1.366341 0.0027983 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 55 2000 5 linear nonlinear 0.7820 0.4412162 0.4356887 0.0055275 0.5008267 0.5011620 1.364197 0.8726 0.8856 0.4400177 1.360925 0.0043289 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 56 2000 5 linear nonlinear 0.7355 0.4432457 0.4342630 0.0089827 0.5015592 0.4982233 1.363893 0.8272 0.8881 0.4382035 1.357548 0.0039405 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 57 2000 5 linear nonlinear 0.7450 0.4437641 0.4329443 0.0108198 0.4998788 0.4986903 1.360731 0.8054 0.9032 0.4361548 1.358653 0.0032105 37 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 58 2000 5 linear nonlinear 0.7610 0.4386121 0.4336829 0.0049292 0.5003243 0.5000030 1.361324 0.8754 0.8978 0.4369567 1.359285 0.0032738 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 59 2000 5 linear nonlinear 0.7730 0.4375696 0.4363067 0.0012629 0.5012040 0.5008751 1.356246 0.9347 0.9410 0.4373414 1.356213 0.0010348 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 60 2000 5 linear nonlinear 0.7625 0.4369214 0.4342196 0.0027018 0.4990987 0.5008384 1.369521 0.9090 0.9088 0.4369444 1.369014 0.0027248 39 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 61 2000 5 linear nonlinear 0.7715 0.4391713 0.4326925 0.0064788 0.4979037 0.5000255 1.364600 0.9255 0.9213 0.4348831 1.361582 0.0021906 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 62 2000 5 linear nonlinear 0.7705 0.4372901 0.4333479 0.0039422 0.5015467 0.4969841 1.359049 0.8934 0.8864 0.4377561 1.352818 0.0044081 37 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 63 2000 5 linear nonlinear 0.7160 0.4345617 0.4334543 0.0011075 0.4982860 0.4982377 1.361391 0.9409 0.9432 0.4344711 1.354526 0.0010168 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 64 2000 5 linear nonlinear 0.7605 0.4403509 0.4346900 0.0056609 0.5000815 0.5000277 1.360920 0.8669 0.9025 0.4377198 1.360705 0.0030298 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 65 2000 5 linear nonlinear 0.7635 0.4405130 0.4340645 0.0064485 0.4986957 0.4999486 1.370000 0.8600 0.8412 0.4421026 1.369193 0.0080381 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 66 2000 5 linear nonlinear 0.7385 0.4363711 0.4347177 0.0016534 0.4995672 0.5005019 1.362746 0.9433 0.9137 0.4370442 1.358713 0.0023265 37 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 67 2000 5 linear nonlinear 0.7265 0.4440837 0.4343166 0.0097671 0.5001462 0.4989534 1.363850 0.8268 0.9372 0.4355786 1.357967 0.0012620 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 68 2000 5 linear nonlinear 0.7595 0.4378898 0.4337339 0.0041559 0.4987417 0.5004946 1.358945 0.8898 0.9231 0.4357762 1.354668 0.0020424 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 69 2000 5 linear nonlinear 0.7275 0.4403910 0.4331309 0.0072602 0.4993466 0.4982130 1.368646 0.8489 0.9140 0.4355674 1.365555 0.0024366 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 70 2000 5 linear nonlinear 0.7360 0.4355852 0.4341821 0.0014031 0.4998059 0.4996911 1.364122 0.9334 0.9540 0.4348622 1.362242 0.0006801 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 71 2000 5 linear nonlinear 0.7555 0.4409245 0.4332034 0.0077212 0.4994938 0.4986528 1.366087 0.8443 0.8931 0.4369358 1.360383 0.0037325 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 72 2000 5 linear nonlinear 0.7670 0.4406269 0.4344979 0.0061290 0.4993990 0.5002631 1.362156 0.8607 0.8691 0.4400388 1.360740 0.0055410 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 73 2000 5 linear nonlinear 0.7635 0.4363269 0.4341047 0.0022223 0.5006040 0.4996455 1.364485 0.9113 0.9191 0.4360165 1.365732 0.0019118 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 74 2000 5 linear nonlinear 0.7695 0.4672715 0.4344561 0.0328154 0.5006751 0.5016707 1.362744 0.7627 0.8665 0.4403764 1.358847 0.0059203 37 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 75 2000 5 linear nonlinear 0.7565 0.4371220 0.4360251 0.0010969 0.5022823 0.5006700 1.361990 0.9422 0.9209 0.4381019 1.360066 0.0020769 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 76 2000 5 linear nonlinear 0.7795 0.4387719 0.4341216 0.0046503 0.5002178 0.5005316 1.359761 0.8852 0.8802 0.4389434 1.361914 0.0048219 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 77 2000 5 linear nonlinear 0.7705 0.4360834 0.4340338 0.0020497 0.5014692 0.4986244 1.354435 0.9171 0.9411 0.4351877 1.354327 0.0011539 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 78 2000 5 linear nonlinear 0.7845 0.4417787 0.4365833 0.0051954 0.5020874 0.5009455 1.364445 0.8590 0.9294 0.4380787 1.363608 0.0014954 32 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 79 2000 5 linear nonlinear 0.7455 0.4481170 0.4360454 0.0120717 0.5026605 0.4982332 1.360067 0.7913 0.8840 0.4404094 1.362236 0.0043640 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 80 2000 5 linear nonlinear 0.7725 0.4378384 0.4343307 0.0035077 0.4997028 0.5008289 1.360132 0.8966 0.9036 0.4375559 1.360602 0.0032253 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 81 2000 5 linear nonlinear 0.7760 0.4402945 0.4355837 0.0047108 0.5000054 0.5017075 1.355182 0.8851 0.8658 0.4412451 1.352354 0.0056614 35 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 82 2000 5 linear nonlinear 0.7360 0.4366809 0.4346371 0.0020438 0.5006233 0.4991992 1.362097 0.9225 0.9272 0.4363824 1.356149 0.0017452 35 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 83 2000 5 linear nonlinear 0.7325 0.4398713 0.4353327 0.0045386 0.4997691 0.5014046 1.362869 0.8835 0.8910 0.4394001 1.358399 0.0040674 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 84 2000 5 linear nonlinear 0.7685 0.4389082 0.4345637 0.0043445 0.5006008 0.4990414 1.371564 0.8842 0.8966 0.4380214 1.367702 0.0034577 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 85 2000 5 linear nonlinear 0.7825 0.4517892 0.4335835 0.0182057 0.4995179 0.4990623 1.365771 0.7939 0.8364 0.4420557 1.360865 0.0084722 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 86 2000 5 linear nonlinear 0.7575 0.4438795 0.4332506 0.0106289 0.4995191 0.4985697 1.370086 0.7770 0.9097 0.4355425 1.365439 0.0022920 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 87 2000 5 linear nonlinear 0.7315 0.4401710 0.4362084 0.0039626 0.5010275 0.5016419 1.365848 0.8858 0.9112 0.4387800 1.364736 0.0025716 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 88 2000 5 linear nonlinear 0.7700 0.4430151 0.4355013 0.0075137 0.5004554 0.5012549 1.359567 0.8376 0.8602 0.4414689 1.356500 0.0059675 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 89 2000 5 linear nonlinear 0.7325 0.4433910 0.4344961 0.0088949 0.4996620 0.4998635 1.366718 0.8324 0.8529 0.4415141 1.362891 0.0070180 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 90 2000 5 linear nonlinear 0.7000 0.4449297 0.4342774 0.0106523 0.4991583 0.5002809 1.357722 0.7963 0.9048 0.4370672 1.351741 0.0027898 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 91 2000 5 linear nonlinear 0.7635 0.4388588 0.4366768 0.0021820 0.5027272 0.5019234 1.361557 0.9211 0.9153 0.4390799 1.361521 0.0024031 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 92 2000 5 linear nonlinear 0.7720 0.4409942 0.4344610 0.0065332 0.5004120 0.4994187 1.357417 0.8295 0.9487 0.4351985 1.358225 0.0007375 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 93 2000 5 linear nonlinear 0.7460 0.4375583 0.4337036 0.0038547 0.4996669 0.5001424 1.361377 0.8920 0.9305 0.4354423 1.359192 0.0017387 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 94 2000 5 linear nonlinear 0.7605 0.4346702 0.4330905 0.0015797 0.4993979 0.4984994 1.363869 0.9286 0.9342 0.4344418 1.359330 0.0013513 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 95 2000 5 linear nonlinear 0.7660 0.4391565 0.4332538 0.0059027 0.5002390 0.4990745 1.356397 0.8647 0.8853 0.4376348 1.355935 0.0043810 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 96 2000 5 linear nonlinear 0.7505 0.4485452 0.4353751 0.0131701 0.5009542 0.5009903 1.363733 0.8682 0.8620 0.4409492 1.361257 0.0055741 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 97 2000 5 linear nonlinear 0.7705 0.4356997 0.4333954 0.0023043 0.4989856 0.4991039 1.351724 0.9153 0.9267 0.4351187 1.348231 0.0017233 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 98 2000 5 linear nonlinear 0.7840 0.4347437 0.4314219 0.0033219 0.4991058 0.4971278 1.360060 0.9060 0.9454 0.4323651 1.356748 0.0009433 32 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 99 2000 5 linear nonlinear 0.7495 0.4373498 0.4351185 0.0022313 0.5020671 0.4997722 1.352775 0.9148 0.9000 0.4382279 1.348673 0.0031094 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
15 100 2000 5 linear nonlinear 0.7420 0.4437960 0.4351392 0.0086568 0.5009239 0.5007765 1.363181 0.8360 0.8719 0.4405907 1.360861 0.0054515 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
16 1 500 10 linear nonlinear 0.6005 0.4717279 0.4343764 0.0373515 0.4993082 0.5010367 1.389367 0.6286 0.7699 0.4507745 1.393378 0.0163980 29 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 2 500 10 linear nonlinear 0.6140 0.4631136 0.4346882 0.0284254 0.5002696 0.4993576 1.382556 0.6868 0.7224 0.4577822 1.395644 0.0230939 27 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 3 500 10 linear nonlinear 0.5115 0.4735003 0.4319890 0.0415113 0.4978385 0.4981347 1.398557 0.6030 0.7490 0.4517524 1.412339 0.0197635 23 0.5 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 4 500 10 linear nonlinear 0.5175 0.4551052 0.4367069 0.0183983 0.5031022 0.5012322 1.392338 0.7559 0.7414 0.4572763 1.395747 0.0205694 21 0.4 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 5 500 10 linear nonlinear 0.6125 0.4771468 0.4347023 0.0424446 0.5003138 0.4994014 1.395442 0.6549 0.7743 0.4505645 1.403159 0.0158622 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 6 500 10 linear nonlinear 0.5545 0.4776357 0.4334376 0.0441982 0.4998676 0.4988347 1.412703 0.6077 0.7686 0.4502646 1.401538 0.0168271 28 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 7 500 10 linear nonlinear 0.5600 0.4410036 0.4327751 0.0082285 0.4995703 0.4982861 1.395668 0.8423 0.8337 0.4420200 1.406833 0.0092449 28 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 8 500 10 linear nonlinear 0.5450 0.4715344 0.4345899 0.0369446 0.5015498 0.5005395 1.380995 0.6472 0.6891 0.4639810 1.395932 0.0293912 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 9 500 10 linear nonlinear 0.6125 0.4593037 0.4344986 0.0248051 0.4998589 0.5001297 1.385259 0.7229 0.7306 0.4568136 1.394546 0.0223150 28 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 10 500 10 linear nonlinear 0.6105 0.4550032 0.4347817 0.0202215 0.5009608 0.4997546 1.392414 0.7518 0.7983 0.4468944 1.382920 0.0121127 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 11 500 10 linear nonlinear 0.5650 0.4633871 0.4332199 0.0301672 0.5002027 0.5003977 1.391003 0.6820 0.8181 0.4434069 1.396242 0.0101870 29 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 12 500 10 linear nonlinear 0.5865 0.4750862 0.4329710 0.0421153 0.5000470 0.4987780 1.426353 0.6384 0.7626 0.4506459 1.416744 0.0176749 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 13 500 10 linear nonlinear 0.5730 0.4556029 0.4355844 0.0200185 0.4993759 0.5014577 1.381798 0.7273 0.8117 0.4467629 1.397760 0.0111785 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 14 500 10 linear nonlinear 0.5925 0.4459365 0.4360715 0.0098649 0.5022341 0.5011885 1.366777 0.8274 0.8524 0.4432253 1.370120 0.0071538 20 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 15 500 10 linear nonlinear 0.5670 0.4833936 0.4346463 0.0487473 0.4990603 0.5010168 1.385770 0.5666 0.8699 0.4402708 1.375996 0.0056245 27 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 16 500 10 linear nonlinear 0.5635 0.4819855 0.4340430 0.0479425 0.5008838 0.4988049 1.411776 0.5803 0.7883 0.4479199 1.399473 0.0138769 23 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 17 500 10 linear nonlinear 0.5400 0.4490374 0.4326499 0.0163875 0.4996468 0.4975311 1.385417 0.7688 0.8135 0.4442425 1.402535 0.0115926 23 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 18 500 10 linear nonlinear 0.5735 0.4546179 0.4354766 0.0191413 0.5003994 0.5017356 1.382548 0.7288 0.8600 0.4417474 1.396714 0.0062708 25 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 19 500 10 linear nonlinear 0.4825 0.4628009 0.4330868 0.0297141 0.5001287 0.4983091 1.409070 0.6764 0.7496 0.4520603 1.408965 0.0189735 30 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 20 500 10 linear nonlinear 0.5340 0.4576998 0.4343941 0.0233057 0.4984505 0.5014562 1.422212 0.7310 0.7793 0.4499752 1.428206 0.0155811 23 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 21 500 10 linear nonlinear 0.5670 0.4511354 0.4340602 0.0170752 0.4989242 0.5006414 1.391077 0.7700 0.7855 0.4487027 1.398636 0.0146425 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 22 500 10 linear nonlinear 0.5600 0.4767232 0.4337078 0.0430155 0.4986539 0.4999833 1.421955 0.6098 0.6952 0.4617385 1.418078 0.0280307 29 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 23 500 10 linear nonlinear 0.5245 0.4784790 0.4362766 0.0422023 0.5004361 0.5014715 1.406310 0.6002 0.7851 0.4509275 1.408058 0.0146509 30 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 24 500 10 linear nonlinear 0.6265 0.4441213 0.4338314 0.0102899 0.4992674 0.4999312 1.389663 0.8274 0.8234 0.4441303 1.413825 0.0102988 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 25 500 10 linear nonlinear 0.5655 0.4513071 0.4352143 0.0160929 0.4987456 0.5032379 1.387069 0.7699 0.8141 0.4467182 1.406845 0.0115039 30 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 26 500 10 linear nonlinear 0.5720 0.4509699 0.4325082 0.0184617 0.4982105 0.4998990 1.418012 0.7621 0.7320 0.4546426 1.435729 0.0221344 32 0.3 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 27 500 10 linear nonlinear 0.5880 0.4840020 0.4330289 0.0509731 0.4994928 0.4985829 1.384219 0.4890 0.8131 0.4441755 1.381607 0.0111466 25 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 28 500 10 linear nonlinear 0.5790 0.4585439 0.4348096 0.0237343 0.4997032 0.5011921 1.404443 0.7255 0.7447 0.4548292 1.405643 0.0200197 25 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 29 500 10 linear nonlinear 0.5740 0.4555206 0.4345966 0.0209240 0.4992976 0.5002497 1.388202 0.7431 0.7287 0.4573003 1.410438 0.0227037 28 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 30 500 10 linear nonlinear 0.5835 0.4486174 0.4336018 0.0150156 0.4998672 0.5004542 1.389690 0.7888 0.7665 0.4505323 1.413003 0.0169305 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 31 500 10 linear nonlinear 0.6015 0.4636691 0.4334156 0.0302534 0.4989051 0.4996009 1.399056 0.6812 0.7214 0.4572516 1.404215 0.0238360 26 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 32 500 10 linear nonlinear 0.5830 0.4779727 0.4347180 0.0432548 0.5011073 0.5013688 1.405469 0.6211 0.6114 0.4787563 1.426766 0.0440384 27 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 33 500 10 linear nonlinear 0.5365 0.4484693 0.4356880 0.0127813 0.5020397 0.4997930 1.391564 0.7972 0.8244 0.4454084 1.396333 0.0097204 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 34 500 10 linear nonlinear 0.5810 0.4761797 0.4337592 0.0424205 0.5013218 0.4996088 1.405387 0.6160 0.7103 0.4597635 1.402003 0.0260043 28 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 35 500 10 linear nonlinear 0.5935 0.4545266 0.4342764 0.0202503 0.4998080 0.5004322 1.397237 0.7374 0.7955 0.4481191 1.422737 0.0138428 28 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 36 500 10 linear nonlinear 0.5370 0.4542483 0.4330337 0.0212146 0.5005282 0.4980725 1.409138 0.7470 0.7241 0.4563702 1.400506 0.0233365 28 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 37 500 10 linear nonlinear 0.5085 0.4431403 0.4345983 0.0085420 0.5024052 0.5001317 1.381779 0.8386 0.7727 0.4509855 1.393236 0.0163872 32 0.5 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 38 500 10 linear nonlinear 0.5815 0.4688621 0.4336053 0.0352568 0.5003114 0.4983096 1.383872 0.6525 0.7509 0.4526058 1.383610 0.0190005 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 39 500 10 linear nonlinear 0.4550 0.4737096 0.4357370 0.0379725 0.5021531 0.5014760 1.399992 0.6459 0.7999 0.4494805 1.405561 0.0137435 28 0.3 0.6 1.0 0.8571429 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 40 500 10 linear nonlinear 0.5605 0.4785079 0.4346677 0.0438402 0.5004321 0.5009186 1.398940 0.6142 0.7057 0.4610402 1.419776 0.0263725 27 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 41 500 10 linear nonlinear 0.5760 0.4542912 0.4329579 0.0213334 0.5008326 0.4979851 1.385033 0.7320 0.8107 0.4441970 1.392698 0.0112391 23 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 42 500 10 linear nonlinear 0.5395 0.4822041 0.4347350 0.0474690 0.4993255 0.5017730 1.404878 0.5837 0.7249 0.4577642 1.406445 0.0230291 18 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 43 500 10 linear nonlinear 0.5985 0.4520529 0.4338649 0.0181880 0.4997836 0.4987823 1.395779 0.7591 0.7412 0.4536058 1.409239 0.0197409 25 0.2 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 44 500 10 linear nonlinear 0.6055 0.4616346 0.4331809 0.0284536 0.5000739 0.4991250 1.381676 0.6915 0.7178 0.4574428 1.388191 0.0242619 21 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 45 500 10 linear nonlinear 0.5570 0.4560159 0.4336547 0.0223613 0.5003429 0.5000006 1.393293 0.7315 0.7553 0.4524539 1.403857 0.0187992 26 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 46 500 10 linear nonlinear 0.4005 0.4684121 0.4358204 0.0325917 0.5015161 0.5012261 1.443164 0.6655 0.7566 0.4542108 1.416062 0.0183905 25 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 47 500 10 linear nonlinear 0.4375 0.4800050 0.4343778 0.0456272 0.5003689 0.4990902 1.434882 0.5968 0.7261 0.4576935 1.436489 0.0233157 24 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 48 500 10 linear nonlinear 0.5305 0.4521057 0.4364069 0.0156989 0.5013930 0.5020826 1.413512 0.7814 0.8068 0.4483310 1.437950 0.0119242 28 0.3 0.7 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 49 500 10 linear nonlinear 0.5590 0.4595593 0.4333823 0.0261771 0.5000572 0.4976312 1.400372 0.7049 0.7994 0.4457905 1.407860 0.0124082 30 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 50 500 10 linear nonlinear 0.5535 0.4544092 0.4320167 0.0223925 0.4992157 0.4987656 1.404554 0.7200 0.8329 0.4414592 1.421008 0.0094425 27 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 51 500 10 linear nonlinear 0.5920 0.4562632 0.4354027 0.0208605 0.5010222 0.5003652 1.401384 0.7422 0.7580 0.4531515 1.406397 0.0177489 25 0.5 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 52 500 10 linear nonlinear 0.5905 0.4768317 0.4354019 0.0414298 0.5010501 0.5003626 1.423389 0.6229 0.6960 0.4632170 1.437155 0.0278151 28 0.7 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 53 500 10 linear nonlinear 0.5930 0.4546630 0.4334032 0.0212598 0.4985236 0.5010352 1.391903 0.7385 0.7344 0.4553152 1.404227 0.0219120 26 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 54 500 10 linear nonlinear 0.5880 0.4440706 0.4351239 0.0089466 0.5014356 0.5012064 1.406604 0.8347 0.8299 0.4441321 1.419168 0.0090081 27 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 55 500 10 linear nonlinear 0.5405 0.4461431 0.4351785 0.0109646 0.4995958 0.5009666 1.383915 0.8176 0.8342 0.4439323 1.407537 0.0087538 28 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 56 500 10 linear nonlinear 0.6160 0.4426567 0.4332999 0.0093568 0.5003413 0.4996221 1.371530 0.8427 0.8479 0.4408390 1.380784 0.0075392 27 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 57 500 10 linear nonlinear 0.5765 0.4782501 0.4352965 0.0429536 0.5002081 0.5009145 1.390225 0.5632 0.8445 0.4432388 1.389416 0.0079423 29 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 58 500 10 linear nonlinear 0.5270 0.4706756 0.4350256 0.0356500 0.4987574 0.5008101 1.397594 0.6435 0.7115 0.4597235 1.408348 0.0246979 26 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 59 500 10 linear nonlinear 0.5670 0.4786131 0.4335601 0.0450529 0.5002793 0.4986354 1.376641 0.6474 0.8052 0.4456458 1.383215 0.0120856 24 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 60 500 10 linear nonlinear 0.5355 0.5012550 0.4342078 0.0670472 0.4998023 0.5005982 1.416461 0.5084 0.6985 0.4620065 1.400131 0.0277987 23 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 61 500 10 linear nonlinear 0.5820 0.4541044 0.4346489 0.0194555 0.5006883 0.4997145 1.392103 0.7393 0.7877 0.4485913 1.412735 0.0139424 28 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 62 500 10 linear nonlinear 0.5335 0.4521973 0.4339648 0.0182325 0.4997805 0.5001498 1.386310 0.7561 0.8247 0.4436763 1.388915 0.0097115 24 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 63 500 10 linear nonlinear 0.5015 0.4536174 0.4337214 0.0198960 0.4993669 0.5006734 1.413413 0.7554 0.7892 0.4479973 1.429115 0.0142759 24 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 64 500 10 linear nonlinear 0.5750 0.4712613 0.4353600 0.0359012 0.4993043 0.5029167 1.389347 0.6465 0.6991 0.4621388 1.393276 0.0267787 22 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 65 500 10 linear nonlinear 0.5430 0.4596254 0.4344359 0.0251896 0.5002500 0.5000851 1.395408 0.7102 0.7050 0.4600180 1.419636 0.0255821 23 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 66 500 10 linear nonlinear 0.5650 0.4956855 0.4337352 0.0619504 0.4998135 0.5002982 1.402566 0.5274 0.6014 0.4800740 1.420614 0.0463389 26 0.2 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 67 500 10 linear nonlinear 0.5755 0.4990349 0.4345456 0.0644893 0.4990349 0.5007974 1.421316 0.5042 0.7512 0.4534053 1.427720 0.0188597 21 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 68 500 10 linear nonlinear 0.4950 0.4894483 0.4348662 0.0545821 0.5003445 0.4989290 1.418284 0.5475 0.7134 0.4593444 1.401476 0.0244782 25 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 69 500 10 linear nonlinear 0.5625 0.4707509 0.4329780 0.0377729 0.4996136 0.4988279 1.393980 0.6001 0.8614 0.4396393 1.400995 0.0066613 22 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 70 500 10 linear nonlinear 0.5795 0.4853263 0.4343870 0.0509393 0.5014366 0.4998322 1.403664 0.5793 0.7474 0.4545703 1.397165 0.0201833 27 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 71 500 10 linear nonlinear 0.5295 0.4629316 0.4362569 0.0266748 0.5023201 0.5010523 1.411830 0.7040 0.8327 0.4454280 1.391452 0.0091711 24 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 72 500 10 linear nonlinear 0.5910 0.4853686 0.4355690 0.0497995 0.5011962 0.5005156 1.378641 0.5621 0.7797 0.4508863 1.374300 0.0153173 24 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 73 500 10 linear nonlinear 0.6215 0.4480373 0.4348259 0.0132114 0.5020220 0.4994173 1.393995 0.8003 0.7819 0.4500415 1.392572 0.0152156 29 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 74 500 10 linear nonlinear 0.5995 0.4561051 0.4350551 0.0210501 0.5005267 0.5007182 1.388314 0.7373 0.7026 0.4614088 1.417004 0.0263537 27 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 75 500 10 linear nonlinear 0.5675 0.4581668 0.4350763 0.0230906 0.5019796 0.4994407 1.374513 0.7250 0.7196 0.4588591 1.390256 0.0237828 24 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 76 500 10 linear nonlinear 0.4640 0.4426728 0.4326438 0.0100290 0.4993186 0.4995314 1.411415 0.8207 0.8660 0.4385265 1.398507 0.0058827 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 77 500 10 linear nonlinear 0.5835 0.4684923 0.4328008 0.0356915 0.4985882 0.4978994 1.377724 0.6435 0.7621 0.4502434 1.381698 0.0174426 27 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 78 500 10 linear nonlinear 0.5605 0.4805783 0.4339942 0.0465841 0.4979580 0.5016490 1.395593 0.6234 0.8054 0.4463448 1.408889 0.0123506 24 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 79 500 10 linear nonlinear 0.5920 0.4695910 0.4341547 0.0354363 0.4991842 0.5010538 1.380975 0.6988 0.8140 0.4450472 1.382000 0.0108926 25 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 80 500 10 linear nonlinear 0.6230 0.4443944 0.4339928 0.0104016 0.4993271 0.4994122 1.386359 0.8232 0.7820 0.4485850 1.409455 0.0145923 24 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 81 500 10 linear nonlinear 0.5620 0.4622666 0.4340637 0.0282029 0.5004403 0.5002663 1.406244 0.6937 0.8128 0.4456051 1.403318 0.0115414 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 82 500 10 linear nonlinear 0.5520 0.4586042 0.4352356 0.0233686 0.5001144 0.4996374 1.395943 0.7807 0.8135 0.4466791 1.409178 0.0114435 27 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 83 500 10 linear nonlinear 0.5945 0.4520259 0.4332662 0.0187597 0.4982142 0.5000555 1.375085 0.7523 0.8510 0.4405993 1.380469 0.0073332 24 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 84 500 10 linear nonlinear 0.5675 0.4507779 0.4360549 0.0147229 0.5008080 0.5004014 1.383463 0.7824 0.7913 0.4499357 1.388330 0.0138808 26 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 85 500 10 linear nonlinear 0.5575 0.4541352 0.4345182 0.0196170 0.5009075 0.5014508 1.392277 0.7467 0.8510 0.4416156 1.400988 0.0070974 25 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 86 500 10 linear nonlinear 0.5360 0.4813135 0.4355322 0.0457813 0.5008017 0.5007235 1.394546 0.6596 0.7975 0.4482614 1.412088 0.0127292 24 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 87 500 10 linear nonlinear 0.5060 0.4604588 0.4353665 0.0250923 0.5000111 0.5021405 1.398251 0.7355 0.7899 0.4489103 1.404710 0.0135438 24 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 88 500 10 linear nonlinear 0.5840 0.4560955 0.4342112 0.0218843 0.5005926 0.4988290 1.385310 0.7331 0.7632 0.4519017 1.400559 0.0176905 29 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 89 500 10 linear nonlinear 0.5890 0.4932854 0.4346423 0.0586431 0.4993742 0.5003887 1.401560 0.5252 0.6913 0.4631623 1.404469 0.0285200 27 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 90 500 10 linear nonlinear 0.4705 0.4798417 0.4343430 0.0454987 0.5002074 0.4998036 1.416156 0.5830 0.8135 0.4455541 1.399030 0.0112111 26 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 91 500 10 linear nonlinear 0.6120 0.4726735 0.4340469 0.0386266 0.5000670 0.4988956 1.395569 0.6524 0.7573 0.4520871 1.391910 0.0180402 22 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 92 500 10 linear nonlinear 0.5585 0.4557325 0.4331235 0.0226090 0.4994242 0.4986381 1.421005 0.7291 0.7683 0.4504989 1.437954 0.0173754 25 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 93 500 10 linear nonlinear 0.6080 0.4581490 0.4347044 0.0234447 0.5012556 0.4977567 1.396796 0.7246 0.7495 0.4544688 1.400459 0.0197644 27 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 94 500 10 linear nonlinear 0.5820 0.4651221 0.4337550 0.0313672 0.4999865 0.5001506 1.391835 0.6957 0.7461 0.4539500 1.392160 0.0201951 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 95 500 10 linear nonlinear 0.5905 0.4701975 0.4345042 0.0356933 0.4997304 0.5010089 1.405169 0.6865 0.7547 0.4530693 1.417605 0.0185651 21 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 96 500 10 linear nonlinear 0.5915 0.4718367 0.4348586 0.0369781 0.5002912 0.5013709 1.384023 0.6466 0.6807 0.4659465 1.388978 0.0310879 27 0.8 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 97 500 10 linear nonlinear 0.5290 0.4612107 0.4339896 0.0272210 0.5009733 0.4988022 1.411055 0.7104 0.7195 0.4582882 1.430001 0.0242986 21 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 98 500 10 linear nonlinear 0.4935 0.4876560 0.4354407 0.0522153 0.5000996 0.5018491 1.397989 0.5463 0.8286 0.4446838 1.385772 0.0092431 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 99 500 10 linear nonlinear 0.5820 0.4856462 0.4366129 0.0490333 0.5023418 0.5011642 1.407557 0.5801 0.6780 0.4679144 1.403401 0.0313015 26 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
16 100 500 10 linear nonlinear 0.5955 0.4404078 0.4341533 0.0062544 0.5003454 0.5004363 1.390716 0.8691 0.8808 0.4387166 1.406282 0.0045632 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=linear, m.choice=nonlinear
17 1 1000 10 linear nonlinear 0.6220 0.4623304 0.4348874 0.0274430 0.5011069 0.4995982 1.373800 0.7377 0.8024 0.4472720 1.376540 0.0123846 30 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 2 1000 10 linear nonlinear 0.5945 0.4424718 0.4340329 0.0084389 0.5007323 0.4995098 1.373352 0.8362 0.8560 0.4407734 1.372616 0.0067405 29 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 3 1000 10 linear nonlinear 0.5950 0.4554901 0.4357305 0.0197596 0.5007606 0.5009211 1.379802 0.7396 0.7851 0.4493688 1.379055 0.0136383 33 0.3 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 4 1000 10 linear nonlinear 0.6370 0.4605898 0.4328508 0.0277391 0.4995299 0.4986567 1.388366 0.7673 0.8257 0.4426801 1.388756 0.0098293 32 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 5 1000 10 linear nonlinear 0.6505 0.4696414 0.4334913 0.0361501 0.4992113 0.4998412 1.371066 0.6407 0.8420 0.4414756 1.368714 0.0079843 29 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 6 1000 10 linear nonlinear 0.6290 0.4669466 0.4343973 0.0325493 0.4991608 0.5018464 1.387958 0.6502 0.7900 0.4481820 1.386577 0.0137847 31 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 7 1000 10 linear nonlinear 0.6290 0.4540237 0.4336235 0.0204002 0.4989519 0.4984361 1.379348 0.7454 0.7578 0.4521550 1.377296 0.0185316 28 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 8 1000 10 linear nonlinear 0.5405 0.4406482 0.4329094 0.0077388 0.4977117 0.4975936 1.394476 0.8436 0.8226 0.4427557 1.401279 0.0098463 33 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 9 1000 10 linear nonlinear 0.6340 0.4453019 0.4340750 0.0112269 0.5003984 0.4986725 1.365609 0.8129 0.8426 0.4418679 1.367051 0.0077929 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 10 1000 10 linear nonlinear 0.6365 0.4425954 0.4342702 0.0083252 0.5001438 0.5009561 1.383046 0.8389 0.8129 0.4454532 1.387708 0.0111829 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 11 1000 10 linear nonlinear 0.6160 0.4472818 0.4334109 0.0138709 0.4977126 0.4996454 1.368430 0.7861 0.8248 0.4431331 1.368752 0.0097221 31 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 12 1000 10 linear nonlinear 0.6355 0.4564576 0.4343187 0.0221388 0.5010730 0.5005781 1.372408 0.7342 0.7392 0.4557273 1.373362 0.0214086 34 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 13 1000 10 linear nonlinear 0.6085 0.4673290 0.4338771 0.0334519 0.5010671 0.4998426 1.370098 0.6513 0.8214 0.4445708 1.368429 0.0106937 30 0.8 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 14 1000 10 linear nonlinear 0.6115 0.4458825 0.4333933 0.0124892 0.5003801 0.4992618 1.381843 0.7996 0.8691 0.4390545 1.376960 0.0056611 31 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 15 1000 10 linear nonlinear 0.6260 0.4608278 0.4353977 0.0254301 0.5009296 0.5015667 1.377739 0.6951 0.7905 0.4495531 1.379661 0.0141554 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 16 1000 10 linear nonlinear 0.6155 0.4596473 0.4338587 0.0257886 0.4999231 0.4999545 1.373852 0.6810 0.8766 0.4386935 1.375502 0.0048348 29 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 17 1000 10 linear nonlinear 0.6460 0.4536883 0.4362004 0.0174878 0.4993485 0.5033306 1.369386 0.7586 0.8023 0.4487150 1.369813 0.0125146 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 18 1000 10 linear nonlinear 0.5980 0.4577593 0.4347540 0.0230053 0.4995593 0.4998233 1.379144 0.7189 0.7941 0.4479390 1.380230 0.0131850 31 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 19 1000 10 linear nonlinear 0.6080 0.4386179 0.4332940 0.0053239 0.4996574 0.4995844 1.377309 0.8732 0.8345 0.4416191 1.384378 0.0083251 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 20 1000 10 linear nonlinear 0.6015 0.4694823 0.4340357 0.0354466 0.5006889 0.4993649 1.374010 0.6338 0.8277 0.4436700 1.377925 0.0096343 34 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 21 1000 10 linear nonlinear 0.6135 0.4462351 0.4350436 0.0111915 0.4997932 0.4999499 1.384685 0.8075 0.7904 0.4479915 1.387912 0.0129479 29 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 22 1000 10 linear nonlinear 0.6075 0.4443579 0.4343311 0.0100268 0.4994116 0.5001585 1.372675 0.8199 0.8694 0.4397294 1.378855 0.0053983 31 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 23 1000 10 linear nonlinear 0.6095 0.4433698 0.4338075 0.0095623 0.4996769 0.4992280 1.373120 0.8189 0.8706 0.4392077 1.366446 0.0054002 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 24 1000 10 linear nonlinear 0.6035 0.4506944 0.4349546 0.0157398 0.4983744 0.5009150 1.401856 0.7708 0.7887 0.4478929 1.421310 0.0129383 39 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 25 1000 10 linear nonlinear 0.6285 0.4625292 0.4334923 0.0290369 0.4995698 0.4996338 1.378568 0.6722 0.8718 0.4389889 1.381207 0.0054967 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 26 1000 10 linear nonlinear 0.6215 0.4440546 0.4364678 0.0075868 0.5000277 0.5038170 1.377749 0.8486 0.8616 0.4426202 1.380310 0.0061524 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 27 1000 10 linear nonlinear 0.5705 0.4410921 0.4346511 0.0064410 0.5013677 0.4994746 1.369523 0.8584 0.8510 0.4419731 1.369521 0.0073220 36 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 28 1000 10 linear nonlinear 0.5910 0.4945390 0.4346926 0.0598464 0.4987639 0.5007839 1.382661 0.4563 0.8089 0.4461596 1.382375 0.0114670 32 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 29 1000 10 linear nonlinear 0.6270 0.4447586 0.4336102 0.0111485 0.4982494 0.5007794 1.366658 0.8074 0.8732 0.4389737 1.369392 0.0053636 34 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 30 1000 10 linear nonlinear 0.6135 0.4469350 0.4348446 0.0120905 0.4998782 0.5005394 1.363978 0.7980 0.8392 0.4428817 1.372506 0.0080372 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 31 1000 10 linear nonlinear 0.6055 0.4486464 0.4348151 0.0138313 0.4995735 0.5012110 1.373749 0.7879 0.8495 0.4417707 1.375533 0.0069557 30 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 32 1000 10 linear nonlinear 0.6185 0.4635847 0.4349627 0.0286220 0.5013043 0.5004681 1.384399 0.6909 0.7742 0.4515474 1.391904 0.0165847 39 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 33 1000 10 linear nonlinear 0.6185 0.4529296 0.4344734 0.0184562 0.4992478 0.5018428 1.377258 0.7573 0.7341 0.4563847 1.379819 0.0219113 37 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 34 1000 10 linear nonlinear 0.5655 0.4511567 0.4345932 0.0165635 0.4998920 0.5007059 1.379863 0.7865 0.8539 0.4414903 1.371385 0.0068971 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 35 1000 10 linear nonlinear 0.6295 0.4583194 0.4349793 0.0233400 0.5005723 0.5010886 1.381115 0.7276 0.7498 0.4545659 1.383275 0.0195866 28 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 36 1000 10 linear nonlinear 0.6000 0.4608769 0.4326581 0.0282188 0.4982853 0.4995974 1.377362 0.7197 0.7749 0.4489085 1.373121 0.0162504 32 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 37 1000 10 linear nonlinear 0.6485 0.4534743 0.4355236 0.0179507 0.5014929 0.4997492 1.374065 0.7576 0.8143 0.4464007 1.374364 0.0108771 30 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 38 1000 10 linear nonlinear 0.6315 0.4530457 0.4358431 0.0172027 0.5001429 0.5013488 1.360862 0.7610 0.8195 0.4462658 1.363342 0.0104228 30 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 39 1000 10 linear nonlinear 0.6025 0.4690589 0.4347484 0.0343105 0.4997670 0.5011386 1.382791 0.6584 0.7928 0.4484104 1.383011 0.0136620 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 40 1000 10 linear nonlinear 0.6245 0.4421498 0.4366595 0.0054903 0.5004423 0.5034792 1.377298 0.8675 0.8632 0.4422934 1.379302 0.0056339 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 41 1000 10 linear nonlinear 0.6190 0.4452143 0.4347190 0.0104953 0.5010495 0.4995131 1.375777 0.8190 0.8239 0.4444138 1.381981 0.0096948 29 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 42 1000 10 linear nonlinear 0.6015 0.4443015 0.4338176 0.0104839 0.4994282 0.4995381 1.379055 0.8254 0.8003 0.4465749 1.380957 0.0127573 29 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 43 1000 10 linear nonlinear 0.6140 0.4562440 0.4348052 0.0214387 0.5008887 0.4985302 1.387238 0.7371 0.7269 0.4574393 1.399844 0.0226341 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 44 1000 10 linear nonlinear 0.6180 0.4405597 0.4351515 0.0054082 0.5004445 0.5008623 1.369593 0.8667 0.8774 0.4398368 1.364219 0.0046853 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 45 1000 10 linear nonlinear 0.6280 0.4613320 0.4354532 0.0258788 0.5002510 0.5012749 1.370971 0.7606 0.8650 0.4409575 1.366118 0.0055043 32 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 46 1000 10 linear nonlinear 0.5915 0.4382417 0.4348846 0.0033571 0.5000095 0.4997187 1.378195 0.9058 0.8845 0.4388560 1.383451 0.0039714 35 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 47 1000 10 linear nonlinear 0.6305 0.4586362 0.4348049 0.0238313 0.4997994 0.5010612 1.385801 0.7214 0.7123 0.4601162 1.393107 0.0253112 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 48 1000 10 linear nonlinear 0.6085 0.4429709 0.4337574 0.0092135 0.5013025 0.4980716 1.383853 0.8293 0.8602 0.4399695 1.387221 0.0062121 39 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 49 1000 10 linear nonlinear 0.5960 0.4525576 0.4345559 0.0180017 0.4996086 0.5001479 1.389489 0.7558 0.8268 0.4438999 1.380079 0.0093440 34 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 50 1000 10 linear nonlinear 0.5990 0.4591609 0.4355507 0.0236102 0.5008611 0.5012731 1.373895 0.7178 0.8783 0.4403993 1.365713 0.0048486 33 0.6 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 51 1000 10 linear nonlinear 0.5880 0.4404107 0.4336864 0.0067243 0.4987192 0.5022022 1.368964 0.8600 0.8713 0.4389644 1.372152 0.0052780 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 52 1000 10 linear nonlinear 0.6600 0.4500048 0.4324281 0.0175767 0.4986883 0.4992949 1.376311 0.7641 0.7719 0.4486732 1.382337 0.0162451 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 53 1000 10 linear nonlinear 0.5175 0.4585551 0.4334101 0.0251450 0.4985616 0.5002610 1.386887 0.7121 0.8036 0.4454909 1.382630 0.0120808 38 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 54 1000 10 linear nonlinear 0.6320 0.4637622 0.4336486 0.0301136 0.5010670 0.4992267 1.379601 0.7121 0.7900 0.4465234 1.379697 0.0128748 28 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 55 1000 10 linear nonlinear 0.5070 0.4525797 0.4353504 0.0172293 0.5010298 0.5009815 1.371879 0.7536 0.8654 0.4408556 1.363346 0.0055051 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 56 1000 10 linear nonlinear 0.5890 0.4519328 0.4347980 0.0171347 0.5014938 0.4998156 1.375107 0.7905 0.8731 0.4398593 1.366781 0.0050612 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 57 1000 10 linear nonlinear 0.6310 0.4399705 0.4335797 0.0063909 0.4994022 0.4996279 1.372313 0.8587 0.8272 0.4429789 1.371520 0.0093992 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 58 1000 10 linear nonlinear 0.6075 0.4397759 0.4348796 0.0048963 0.5009043 0.4999003 1.372012 0.8779 0.8662 0.4404453 1.373498 0.0055656 30 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 59 1000 10 linear nonlinear 0.6140 0.4720316 0.4358241 0.0362076 0.5020726 0.5018992 1.371984 0.6972 0.8109 0.4473014 1.365863 0.0114773 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 60 1000 10 linear nonlinear 0.6280 0.4371060 0.4329445 0.0041616 0.4990545 0.4984281 1.372569 0.8908 0.8877 0.4370779 1.380522 0.0041334 30 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 61 1000 10 linear nonlinear 0.5220 0.4560146 0.4350291 0.0209855 0.5002597 0.5006182 1.390134 0.7415 0.8087 0.4468389 1.380823 0.0118098 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 62 1000 10 linear nonlinear 0.5980 0.4413400 0.4346378 0.0067022 0.4996917 0.4983630 1.368907 0.8605 0.8502 0.4417409 1.368154 0.0071030 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 63 1000 10 linear nonlinear 0.6135 0.4455265 0.4352165 0.0103100 0.5001481 0.5012829 1.377780 0.8061 0.8748 0.4399138 1.384443 0.0046973 42 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 64 1000 10 linear nonlinear 0.6265 0.4429970 0.4337675 0.0092294 0.4986954 0.5007107 1.373020 0.8345 0.8760 0.4385614 1.370364 0.0047938 31 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 65 1000 10 linear nonlinear 0.6305 0.4429797 0.4341992 0.0087805 0.4983034 0.5010689 1.369822 0.8339 0.8340 0.4426569 1.369513 0.0084577 27 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 66 1000 10 linear nonlinear 0.6150 0.4630246 0.4333784 0.0296462 0.5010864 0.4972750 1.373157 0.6694 0.8230 0.4431307 1.371024 0.0097523 33 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 67 1000 10 linear nonlinear 0.6230 0.4435476 0.4342901 0.0092575 0.4996876 0.5003034 1.373343 0.8312 0.8719 0.4396033 1.373534 0.0053132 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 68 1000 10 linear nonlinear 0.5340 0.4844970 0.4356750 0.0488220 0.5003706 0.5009525 1.394951 0.5573 0.8353 0.4441538 1.374740 0.0084789 34 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 69 1000 10 linear nonlinear 0.6260 0.4562387 0.4350679 0.0211707 0.5006804 0.5007836 1.377207 0.7418 0.7578 0.4535143 1.374398 0.0184464 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 70 1000 10 linear nonlinear 0.6110 0.4461472 0.4345144 0.0116328 0.5006444 0.4987256 1.376663 0.8124 0.7477 0.4530675 1.371510 0.0185531 26 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 71 1000 10 linear nonlinear 0.6155 0.4728529 0.4336244 0.0392285 0.4994929 0.4995356 1.378502 0.6172 0.8159 0.4448321 1.366923 0.0112077 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 72 1000 10 linear nonlinear 0.5780 0.4568623 0.4334140 0.0234483 0.4991972 0.4990425 1.375627 0.7276 0.7230 0.4571490 1.374893 0.0237350 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 73 1000 10 linear nonlinear 0.6180 0.4510251 0.4362254 0.0147997 0.5013330 0.5027353 1.380770 0.7851 0.8071 0.4481764 1.379770 0.0119510 29 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 74 1000 10 linear nonlinear 0.6205 0.4399993 0.4352275 0.0047718 0.5009923 0.4992851 1.367377 0.8845 0.8970 0.4386755 1.374639 0.0034480 29 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 75 1000 10 linear nonlinear 0.6135 0.4401186 0.4336787 0.0064400 0.4985963 0.4984009 1.361929 0.8568 0.9190 0.4357530 1.358338 0.0020743 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 76 1000 10 linear nonlinear 0.6025 0.4444425 0.4331958 0.0112467 0.4996084 0.4992081 1.385509 0.8136 0.8259 0.4431685 1.380022 0.0099726 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 77 1000 10 linear nonlinear 0.5980 0.4444706 0.4338571 0.0106136 0.5015037 0.4983851 1.378914 0.8099 0.8928 0.4376565 1.382304 0.0037994 38 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 78 1000 10 linear nonlinear 0.5900 0.4496426 0.4350290 0.0146136 0.5001082 0.5011946 1.371635 0.7880 0.8003 0.4474102 1.376572 0.0123812 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 79 1000 10 linear nonlinear 0.5700 0.4489349 0.4342900 0.0146449 0.5004455 0.4990830 1.379688 0.7815 0.8032 0.4465482 1.379128 0.0122583 32 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 80 1000 10 linear nonlinear 0.5985 0.4486694 0.4358185 0.0128509 0.4986130 0.5034088 1.386513 0.7979 0.8247 0.4456247 1.388370 0.0098062 28 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 81 1000 10 linear nonlinear 0.6445 0.4435874 0.4344526 0.0091348 0.4997341 0.5002706 1.372120 0.8370 0.8755 0.4396482 1.366094 0.0051956 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 82 1000 10 linear nonlinear 0.6165 0.4529322 0.4326074 0.0203248 0.4979286 0.4986638 1.373426 0.7219 0.8389 0.4409782 1.379449 0.0083708 37 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 83 1000 10 linear nonlinear 0.5825 0.4759371 0.4344128 0.0415243 0.4989667 0.5013078 1.396302 0.6208 0.8838 0.4386459 1.384100 0.0042331 29 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 84 1000 10 linear nonlinear 0.6310 0.4459912 0.4343967 0.0115944 0.4980138 0.5025257 1.377107 0.8118 0.8310 0.4438688 1.379292 0.0094720 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 85 1000 10 linear nonlinear 0.6130 0.4559020 0.4345622 0.0213398 0.4982827 0.5022871 1.378659 0.7393 0.7261 0.4575294 1.379646 0.0229672 37 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 86 1000 10 linear nonlinear 0.6035 0.4507348 0.4350607 0.0156741 0.5008767 0.5002545 1.390685 0.7763 0.7883 0.4489420 1.391488 0.0138813 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 87 1000 10 linear nonlinear 0.6375 0.4705008 0.4349904 0.0355104 0.4998429 0.5005366 1.376009 0.7041 0.8555 0.4411973 1.379352 0.0062068 34 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 88 1000 10 linear nonlinear 0.6325 0.4446604 0.4333663 0.0112941 0.4991739 0.5004811 1.379390 0.8155 0.7910 0.4476094 1.385431 0.0142431 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 89 1000 10 linear nonlinear 0.5930 0.4806523 0.4333213 0.0473310 0.4993805 0.4985839 1.384444 0.5346 0.8630 0.4398074 1.377471 0.0064861 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 90 1000 10 linear nonlinear 0.5515 0.4724218 0.4326452 0.0397766 0.5001698 0.4979397 1.383657 0.6258 0.7463 0.4533644 1.381444 0.0207192 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 91 1000 10 linear nonlinear 0.5935 0.4425677 0.4348913 0.0076763 0.4995177 0.5020730 1.382843 0.8447 0.8621 0.4409188 1.392179 0.0060274 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 92 1000 10 linear nonlinear 0.6380 0.4520726 0.4337432 0.0183293 0.4993494 0.4993913 1.382806 0.7451 0.8797 0.4385604 1.379465 0.0048171 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 93 1000 10 linear nonlinear 0.6425 0.4507573 0.4352211 0.0155363 0.4993540 0.5020881 1.367829 0.8336 0.8798 0.4401351 1.370167 0.0049140 36 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 94 1000 10 linear nonlinear 0.6270 0.4398752 0.4350364 0.0048388 0.5000636 0.5001629 1.363775 0.8827 0.8831 0.4396908 1.364138 0.0046544 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 95 1000 10 linear nonlinear 0.6105 0.4432518 0.4334173 0.0098344 0.4999617 0.4991958 1.379422 0.8232 0.8308 0.4424141 1.380288 0.0089968 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 96 1000 10 linear nonlinear 0.5915 0.4489270 0.4337361 0.0151909 0.5007757 0.4975586 1.375267 0.7783 0.7675 0.4506171 1.384536 0.0168810 34 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 97 1000 10 linear nonlinear 0.5595 0.4673619 0.4334048 0.0339571 0.4988586 0.5004581 1.388669 0.7027 0.7316 0.4561990 1.391116 0.0227942 29 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 98 1000 10 linear nonlinear 0.5945 0.4448572 0.4336605 0.0111966 0.4997797 0.4987625 1.378709 0.8144 0.8178 0.4443533 1.390763 0.0106927 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 99 1000 10 linear nonlinear 0.6425 0.4509224 0.4350458 0.0158767 0.4988032 0.5008341 1.375905 0.7686 0.8228 0.4450103 1.383243 0.0099645 27 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
17 100 1000 10 linear nonlinear 0.6220 0.4590953 0.4355239 0.0235713 0.5004636 0.5006537 1.384562 0.7135 0.8215 0.4452734 1.384894 0.0097494 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=linear, m.choice=nonlinear
18 1 2000 10 linear nonlinear 0.6300 0.4402458 0.4342963 0.0059495 0.4989733 0.5001087 1.362993 0.8622 0.8805 0.4390162 1.361046 0.0047199 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 2 2000 10 linear nonlinear 0.6255 0.4476095 0.4346418 0.0129676 0.5003326 0.5002543 1.365438 0.7676 0.8929 0.4381733 1.365678 0.0035314 44 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 3 2000 10 linear nonlinear 0.6165 0.4441621 0.4332480 0.0109141 0.4983407 0.5008914 1.369490 0.7999 0.9092 0.4358158 1.370767 0.0025678 43 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 4 2000 10 linear nonlinear 0.6285 0.4426341 0.4336297 0.0090044 0.4994672 0.4992155 1.366579 0.8395 0.8736 0.4387501 1.361925 0.0051204 38 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 5 2000 10 linear nonlinear 0.6330 0.4396443 0.4332477 0.0063965 0.4993991 0.5003630 1.369826 0.8664 0.8764 0.4383106 1.366363 0.0050628 43 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 6 2000 10 linear nonlinear 0.6305 0.4422649 0.4346751 0.0075898 0.5006312 0.5001146 1.366231 0.8481 0.8818 0.4393523 1.366904 0.0046772 40 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 7 2000 10 linear nonlinear 0.6400 0.4447131 0.4334496 0.0112635 0.5006656 0.4979025 1.370354 0.8161 0.8474 0.4413110 1.371727 0.0078615 40 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 8 2000 10 linear nonlinear 0.5765 0.4487299 0.4338269 0.0149030 0.4999065 0.4986304 1.362816 0.7705 0.8540 0.4405251 1.363896 0.0066982 36 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 9 2000 10 linear nonlinear 0.5770 0.4450588 0.4363527 0.0087060 0.5009113 0.5016669 1.377858 0.8299 0.8784 0.4411193 1.380347 0.0047666 39 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 10 2000 10 linear nonlinear 0.6210 0.4407248 0.4344396 0.0062852 0.5004354 0.4996858 1.359518 0.8526 0.9015 0.4375994 1.358834 0.0031598 39 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 11 2000 10 linear nonlinear 0.6260 0.4559546 0.4349263 0.0210283 0.5005872 0.5002717 1.369324 0.7395 0.7542 0.4538348 1.369367 0.0189086 38 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 12 2000 10 linear nonlinear 0.6205 0.4429814 0.4342099 0.0087716 0.5008426 0.4995056 1.366037 0.8342 0.8378 0.4425548 1.368482 0.0083449 45 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 13 2000 10 linear nonlinear 0.6265 0.4445285 0.4351050 0.0094236 0.5004342 0.5003464 1.362401 0.8285 0.8172 0.4454961 1.361013 0.0103912 47 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 14 2000 10 linear nonlinear 0.6225 0.4581464 0.4339422 0.0242042 0.5012379 0.4995155 1.365509 0.7002 0.9033 0.4371329 1.362653 0.0031906 39 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 15 2000 10 linear nonlinear 0.5735 0.4416760 0.4339961 0.0076800 0.5004656 0.4996935 1.357107 0.8365 0.8843 0.4383676 1.353837 0.0043715 42 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 16 2000 10 linear nonlinear 0.6405 0.4422373 0.4349754 0.0072619 0.5004591 0.5015625 1.368267 0.8477 0.8593 0.4412188 1.369554 0.0062434 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 17 2000 10 linear nonlinear 0.6280 0.4407793 0.4332766 0.0075027 0.4994335 0.4989598 1.362802 0.8508 0.8812 0.4379459 1.363084 0.0046693 47 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 18 2000 10 linear nonlinear 0.6315 0.4452669 0.4335181 0.0117488 0.4999561 0.4991951 1.369054 0.8093 0.8329 0.4427487 1.368957 0.0092306 43 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 19 2000 10 linear nonlinear 0.6380 0.4409791 0.4352588 0.0057204 0.5004831 0.5009824 1.371534 0.8633 0.8612 0.4411098 1.373382 0.0058510 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 20 2000 10 linear nonlinear 0.6365 0.4396910 0.4341307 0.0055604 0.4991250 0.5010737 1.362791 0.8799 0.8943 0.4377554 1.357233 0.0036247 40 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 21 2000 10 linear nonlinear 0.6315 0.4381246 0.4338250 0.0042996 0.5000259 0.4989173 1.367432 0.8797 0.8983 0.4370724 1.370347 0.0032474 44 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 22 2000 10 linear nonlinear 0.6155 0.4479864 0.4347688 0.0132176 0.4991819 0.5028556 1.366347 0.7984 0.8481 0.4421103 1.360930 0.0073415 44 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 23 2000 10 linear nonlinear 0.5120 0.4530719 0.4353838 0.0176881 0.5017167 0.5011481 1.375297 0.7609 0.8096 0.4465672 1.369610 0.0111834 36 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 24 2000 10 linear nonlinear 0.6395 0.4463518 0.4336457 0.0127061 0.4985617 0.5014583 1.366521 0.7936 0.8637 0.4399316 1.365230 0.0062859 37 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 25 2000 10 linear nonlinear 0.6410 0.4476663 0.4350730 0.0125933 0.5006835 0.5015673 1.366078 0.8029 0.8460 0.4430641 1.365840 0.0079911 47 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 26 2000 10 linear nonlinear 0.6295 0.4393317 0.4344521 0.0048796 0.5014492 0.5006108 1.357746 0.8854 0.8838 0.4390874 1.356688 0.0046353 42 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 27 2000 10 linear nonlinear 0.6270 0.4581808 0.4343674 0.0238134 0.5005302 0.4998427 1.366870 0.6848 0.8913 0.4381598 1.363178 0.0037925 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 28 2000 10 linear nonlinear 0.6285 0.4410981 0.4345975 0.0065006 0.5000367 0.5004612 1.368680 0.8631 0.8331 0.4435395 1.367954 0.0089419 41 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 29 2000 10 linear nonlinear 0.5035 0.4522079 0.4347629 0.0174450 0.4990764 0.5011741 1.374059 0.7633 0.8884 0.4387566 1.369699 0.0039937 41 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 30 2000 10 linear nonlinear 0.6380 0.4463951 0.4346350 0.0117601 0.4986989 0.5013962 1.364174 0.8112 0.9127 0.4370773 1.356107 0.0024422 37 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 31 2000 10 linear nonlinear 0.6080 0.4532657 0.4351594 0.0181063 0.4996000 0.5008458 1.362738 0.7552 0.8423 0.4431926 1.357541 0.0080333 44 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 32 2000 10 linear nonlinear 0.5720 0.4436061 0.4342056 0.0094005 0.5001668 0.5003051 1.365960 0.8294 0.8249 0.4433484 1.365439 0.0091428 47 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 33 2000 10 linear nonlinear 0.6415 0.4479617 0.4333098 0.0146519 0.5003426 0.4996769 1.369363 0.7746 0.8487 0.4404009 1.367716 0.0070911 41 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 34 2000 10 linear nonlinear 0.6385 0.4401519 0.4347278 0.0054241 0.5003015 0.4997335 1.361176 0.8685 0.8727 0.4398029 1.360818 0.0050751 44 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 35 2000 10 linear nonlinear 0.6340 0.4439057 0.4341284 0.0097773 0.4994983 0.5002256 1.365086 0.8288 0.8724 0.4396716 1.366875 0.0055432 41 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 36 2000 10 linear nonlinear 0.6045 0.4502777 0.4337693 0.0165084 0.5000991 0.5000711 1.371695 0.7719 0.7948 0.4475138 1.373475 0.0137444 40 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 37 2000 10 linear nonlinear 0.6125 0.4448119 0.4352567 0.0095552 0.5017479 0.5001108 1.361501 0.8241 0.8626 0.4414031 1.361991 0.0061464 39 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 38 2000 10 linear nonlinear 0.6155 0.4372380 0.4341990 0.0030390 0.5015674 0.5001078 1.366453 0.9058 0.9115 0.4368435 1.366877 0.0026445 33 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 39 2000 10 linear nonlinear 0.6240 0.4433459 0.4341068 0.0092391 0.5002996 0.4979247 1.373359 0.8277 0.8499 0.4410673 1.375059 0.0069605 37 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 40 2000 10 linear nonlinear 0.6015 0.4559365 0.4337936 0.0221429 0.4999454 0.4994145 1.363436 0.7202 0.8247 0.4439881 1.362134 0.0101945 36 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 41 2000 10 linear nonlinear 0.6340 0.4387458 0.4333377 0.0054081 0.5001913 0.4987117 1.361495 0.8757 0.8554 0.4401634 1.357778 0.0068257 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 42 2000 10 linear nonlinear 0.6735 0.4427516 0.4340370 0.0087145 0.4994785 0.4992380 1.368172 0.8340 0.8532 0.4407924 1.365650 0.0067554 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 43 2000 10 linear nonlinear 0.5795 0.4388548 0.4320335 0.0068213 0.4991818 0.4972606 1.363029 0.8486 0.8993 0.4352418 1.363934 0.0032083 40 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 44 2000 10 linear nonlinear 0.6520 0.4391620 0.4333226 0.0058395 0.4987072 0.4998679 1.361588 0.8641 0.8682 0.4388465 1.361689 0.0055239 39 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 45 2000 10 linear nonlinear 0.6175 0.4384952 0.4343476 0.0041476 0.4989027 0.5011128 1.368262 0.8831 0.8921 0.4379386 1.368165 0.0035910 37 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 46 2000 10 linear nonlinear 0.5970 0.4361820 0.4337859 0.0023961 0.4999665 0.4981773 1.360701 0.9169 0.9128 0.4363405 1.363728 0.0025546 47 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 47 2000 10 linear nonlinear 0.5915 0.4358897 0.4340231 0.0018666 0.5001488 0.4991505 1.358640 0.9216 0.9207 0.4360927 1.354688 0.0020696 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 48 2000 10 linear nonlinear 0.6550 0.4422103 0.4330046 0.0092057 0.4971022 0.5005073 1.366734 0.8302 0.8011 0.4449746 1.367936 0.0119701 37 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 49 2000 10 linear nonlinear 0.6315 0.4444690 0.4349672 0.0095018 0.5010787 0.5002312 1.371038 0.8331 0.8425 0.4430781 1.369233 0.0081109 42 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 50 2000 10 linear nonlinear 0.6330 0.4554073 0.4335697 0.0218376 0.5007551 0.4997874 1.365938 0.7028 0.9113 0.4361777 1.362268 0.0026079 46 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 51 2000 10 linear nonlinear 0.6220 0.4439941 0.4354657 0.0085283 0.5003230 0.5029273 1.365075 0.8378 0.8525 0.4425388 1.364420 0.0070731 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 52 2000 10 linear nonlinear 0.6560 0.4425409 0.4324773 0.0100635 0.4990567 0.4993712 1.362685 0.8224 0.8449 0.4401492 1.361944 0.0076719 38 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 53 2000 10 linear nonlinear 0.6180 0.4435166 0.4350424 0.0084742 0.5002012 0.5015563 1.363887 0.8360 0.8568 0.4415249 1.363024 0.0064825 40 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 54 2000 10 linear nonlinear 0.5990 0.4556453 0.4362463 0.0193990 0.5015279 0.5021821 1.368771 0.7402 0.8874 0.4404561 1.366011 0.0042099 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 55 2000 10 linear nonlinear 0.6015 0.4612750 0.4342464 0.0270286 0.4987313 0.5008326 1.371800 0.6829 0.8702 0.4397017 1.367840 0.0054554 45 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 56 2000 10 linear nonlinear 0.6425 0.4385473 0.4347347 0.0038126 0.5006935 0.5013178 1.360103 0.8936 0.9154 0.4372253 1.357988 0.0024906 42 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 57 2000 10 linear nonlinear 0.6500 0.4506778 0.4341020 0.0165759 0.4994602 0.5001170 1.367269 0.7685 0.8114 0.4453245 1.366280 0.0112226 43 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 58 2000 10 linear nonlinear 0.6360 0.4391614 0.4322064 0.0069550 0.4986798 0.4969592 1.365635 0.8508 0.8669 0.4375596 1.362481 0.0053532 39 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 59 2000 10 linear nonlinear 0.6550 0.4406591 0.4350302 0.0056289 0.5003171 0.5014626 1.366237 0.8745 0.8829 0.4395677 1.366529 0.0045375 43 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 60 2000 10 linear nonlinear 0.6250 0.4393040 0.4343270 0.0049771 0.5003425 0.5004277 1.355108 0.8753 0.8399 0.4425908 1.356371 0.0082638 37 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 61 2000 10 linear nonlinear 0.6285 0.4380385 0.4343748 0.0036637 0.5013001 0.4989043 1.369929 0.8959 0.9113 0.4367030 1.367850 0.0023282 41 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 62 2000 10 linear nonlinear 0.6370 0.4388876 0.4340495 0.0048381 0.4999170 0.4993251 1.365496 0.8814 0.8820 0.4386163 1.362340 0.0045668 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 63 2000 10 linear nonlinear 0.6385 0.4437675 0.4363906 0.0073769 0.5011126 0.5021375 1.371021 0.8494 0.8443 0.4441897 1.368677 0.0077991 47 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 64 2000 10 linear nonlinear 0.6335 0.4460575 0.4338307 0.0122268 0.5004904 0.4999087 1.363917 0.8458 0.8866 0.4380790 1.358273 0.0042483 40 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 65 2000 10 linear nonlinear 0.6145 0.4612334 0.4338905 0.0273429 0.5001701 0.4989678 1.373400 0.6832 0.8176 0.4440788 1.367830 0.0101884 35 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 66 2000 10 linear nonlinear 0.6035 0.4429651 0.4340480 0.0089171 0.5007252 0.4993980 1.369633 0.8337 0.8585 0.4404423 1.365123 0.0063943 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 67 2000 10 linear nonlinear 0.5970 0.4514854 0.4345409 0.0169444 0.4987215 0.5005936 1.370616 0.7659 0.8699 0.4400564 1.366193 0.0055154 42 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 68 2000 10 linear nonlinear 0.6450 0.4394859 0.4345348 0.0049510 0.4993413 0.5011454 1.361541 0.8740 0.8695 0.4398671 1.360417 0.0053323 40 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 69 2000 10 linear nonlinear 0.6245 0.4381110 0.4334055 0.0047055 0.4993562 0.4974985 1.365354 0.8813 0.8972 0.4369558 1.361464 0.0035503 40 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 70 2000 10 linear nonlinear 0.6135 0.4455129 0.4335763 0.0119366 0.4989925 0.5005952 1.367400 0.8045 0.8510 0.4406847 1.365983 0.0071084 44 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 71 2000 10 linear nonlinear 0.6135 0.4418336 0.4349042 0.0069295 0.5002271 0.5008694 1.373341 0.8526 0.8679 0.4403211 1.374288 0.0054169 49 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 72 2000 10 linear nonlinear 0.5915 0.4415367 0.4329369 0.0085998 0.4994897 0.4998240 1.366876 0.8332 0.9225 0.4350914 1.366076 0.0021545 39 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 73 2000 10 linear nonlinear 0.6205 0.4427476 0.4344456 0.0083020 0.5016944 0.4988926 1.371205 0.8410 0.8588 0.4408766 1.371070 0.0064311 40 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 74 2000 10 linear nonlinear 0.6145 0.4403316 0.4338350 0.0064966 0.5004370 0.4989126 1.363390 0.8535 0.9112 0.4364799 1.363617 0.0026449 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 75 2000 10 linear nonlinear 0.6370 0.4392805 0.4350998 0.0041807 0.5003325 0.5007478 1.366516 0.8884 0.8960 0.4386147 1.365735 0.0035149 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 76 2000 10 linear nonlinear 0.6025 0.4399764 0.4336168 0.0063596 0.5002229 0.4983652 1.368548 0.8628 0.8794 0.4386044 1.370218 0.0049875 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 77 2000 10 linear nonlinear 0.6315 0.4377905 0.4325692 0.0052213 0.4979004 0.4983578 1.354567 0.8680 0.9152 0.4350221 1.352702 0.0024529 41 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 78 2000 10 linear nonlinear 0.5830 0.4481014 0.4325797 0.0155217 0.4991478 0.4979939 1.373188 0.7592 0.8746 0.4377621 1.360990 0.0051824 42 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 79 2000 10 linear nonlinear 0.6485 0.4408544 0.4339499 0.0069045 0.5015484 0.4999762 1.368116 0.8574 0.8451 0.4419556 1.366816 0.0080057 44 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 80 2000 10 linear nonlinear 0.6245 0.4518386 0.4344999 0.0173386 0.5009445 0.4996357 1.374794 0.7602 0.7771 0.4494642 1.370987 0.0149642 41 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 81 2000 10 linear nonlinear 0.6355 0.4571421 0.4325360 0.0246061 0.4988150 0.4999942 1.369183 0.6953 0.8650 0.4384811 1.362639 0.0059451 35 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 82 2000 10 linear nonlinear 0.6210 0.4411390 0.4340884 0.0070505 0.5021473 0.4994268 1.369087 0.8530 0.8604 0.4403286 1.366435 0.0062402 42 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 83 2000 10 linear nonlinear 0.6325 0.4445735 0.4321035 0.0124700 0.4996599 0.4987189 1.369121 0.8050 0.8332 0.4411081 1.366529 0.0090046 29 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 84 2000 10 linear nonlinear 0.6080 0.4449771 0.4340053 0.0109718 0.4995194 0.4994732 1.367443 0.8089 0.8416 0.4419011 1.366496 0.0078958 48 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 85 2000 10 linear nonlinear 0.6220 0.4408527 0.4347250 0.0061277 0.5006434 0.5004635 1.368098 0.8592 0.9075 0.4376265 1.367636 0.0029015 39 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 86 2000 10 linear nonlinear 0.6450 0.4492374 0.4330127 0.0162247 0.4997376 0.4980192 1.359081 0.7712 0.8502 0.4402710 1.359150 0.0072583 42 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 87 2000 10 linear nonlinear 0.5920 0.4443753 0.4326525 0.0117228 0.5000029 0.4976860 1.370390 0.8050 0.8430 0.4406854 1.367945 0.0080329 37 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 88 2000 10 linear nonlinear 0.6540 0.4423055 0.4347142 0.0075913 0.4997931 0.4999263 1.360281 0.8426 0.8742 0.4397283 1.359667 0.0050141 44 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 89 2000 10 linear nonlinear 0.6385 0.4502787 0.4353536 0.0149250 0.5009168 0.5015858 1.369665 0.7761 0.8621 0.4414889 1.368316 0.0061353 48 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 90 2000 10 linear nonlinear 0.6275 0.4413575 0.4343929 0.0069646 0.5001658 0.4989790 1.368558 0.8664 0.8896 0.4382908 1.369077 0.0038979 39 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 91 2000 10 linear nonlinear 0.6595 0.4559617 0.4332189 0.0227427 0.4997796 0.4998955 1.372204 0.8149 0.8785 0.4382324 1.369646 0.0050134 46 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 92 2000 10 linear nonlinear 0.6300 0.4568372 0.4351717 0.0216655 0.5000598 0.5011507 1.365341 0.7146 0.8747 0.4405682 1.363102 0.0053965 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 93 2000 10 linear nonlinear 0.6145 0.4409724 0.4328033 0.0081691 0.4983297 0.4994850 1.361427 0.8352 0.8857 0.4368607 1.364580 0.0040574 33 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 94 2000 10 linear nonlinear 0.6025 0.4435061 0.4343226 0.0091834 0.5010026 0.5016000 1.362530 0.8283 0.9010 0.4375988 1.363481 0.0032762 37 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 95 2000 10 linear nonlinear 0.6270 0.4496721 0.4335176 0.0161545 0.5006622 0.4995855 1.375126 0.7662 0.8381 0.4420515 1.371786 0.0085339 38 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 96 2000 10 linear nonlinear 0.6280 0.4411759 0.4338423 0.0073336 0.4990685 0.4999816 1.361906 0.8446 0.9122 0.4363720 1.362241 0.0025297 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 97 2000 10 linear nonlinear 0.6175 0.4374731 0.4329393 0.0045338 0.4988901 0.4971727 1.369358 0.8857 0.8882 0.4369929 1.371118 0.0040536 37 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 98 2000 10 linear nonlinear 0.6315 0.4467129 0.4342050 0.0125079 0.4988098 0.5008225 1.364855 0.7368 0.9311 0.4358374 1.364786 0.0016324 33 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 99 2000 10 linear nonlinear 0.6380 0.4639695 0.4339037 0.0300657 0.4996286 0.5001351 1.369135 0.6764 0.8240 0.4437828 1.366965 0.0098790 45 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
18 100 2000 10 linear nonlinear 0.5875 0.4397055 0.4325780 0.0071275 0.4990827 0.4972564 1.362319 0.8548 0.8759 0.4375900 1.360232 0.0050120 45 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=linear, m.choice=nonlinear
19 1 500 5 nonlinear nonlinear 0.7245 0.4248897 0.4202787 0.0046110 0.5283332 0.4726517 1.362850 0.9124 0.5781 0.4787875 1.392486 0.0585088 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 2 500 5 nonlinear nonlinear 0.7375 0.4598772 0.4196043 0.0402729 0.5255408 0.4728516 1.387303 0.6669 0.5915 0.4753658 1.396721 0.0557616 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 3 500 5 nonlinear nonlinear 0.7100 0.4758525 0.4220576 0.0537949 0.5278697 0.4747160 1.410556 0.6094 0.6137 0.4754913 1.406197 0.0534337 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 4 500 5 nonlinear nonlinear 0.7260 0.4271416 0.4209681 0.0061734 0.5286262 0.4731325 1.353962 0.8968 0.6521 0.4681858 1.380311 0.0472177 21 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 5 500 5 nonlinear nonlinear 0.7100 0.4525990 0.4221053 0.0304937 0.5290005 0.4742572 1.398283 0.7496 0.6787 0.4646129 1.416831 0.0425076 20 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 6 500 5 nonlinear nonlinear 0.7115 0.4646062 0.4219012 0.0427050 0.5268706 0.4759204 1.376525 0.6511 0.5650 0.4787724 1.394502 0.0568712 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 7 500 5 nonlinear nonlinear 0.7140 0.4852688 0.4211236 0.0641451 0.5262118 0.4741766 1.406011 0.5350 0.5488 0.4809939 1.403646 0.0598703 27 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 8 500 5 nonlinear nonlinear 0.6715 0.4942407 0.4198099 0.0744307 0.5254498 0.4734806 1.404963 0.5616 0.5601 0.4891643 1.416007 0.0693544 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 9 500 5 nonlinear nonlinear 0.7340 0.4432766 0.4213608 0.0219158 0.5261732 0.4744360 1.371260 0.7899 0.6092 0.4742615 1.381451 0.0529007 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 10 500 5 nonlinear nonlinear 0.7310 0.4406519 0.4208381 0.0198138 0.5253882 0.4743500 1.368602 0.7975 0.6637 0.4650158 1.377647 0.0441777 18 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 11 500 5 nonlinear nonlinear 0.7175 0.4504909 0.4221727 0.0283182 0.5270814 0.4760320 1.375836 0.7783 0.6240 0.4731375 1.404391 0.0509648 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 12 500 5 nonlinear nonlinear 0.7590 0.4518843 0.4204812 0.0314031 0.5263210 0.4735852 1.366045 0.7333 0.6349 0.4696910 1.391966 0.0492098 21 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 13 500 5 nonlinear nonlinear 0.7490 0.4813943 0.4207714 0.0606229 0.5261685 0.4743026 1.394821 0.5676 0.5547 0.4802489 1.398195 0.0594774 20 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 14 500 5 nonlinear nonlinear 0.7165 0.4740925 0.4225927 0.0514998 0.5273930 0.4761817 1.393287 0.5804 0.6008 0.4777549 1.391346 0.0551622 24 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 15 500 5 nonlinear nonlinear 0.7530 0.4686723 0.4215450 0.0471273 0.5270665 0.4745068 1.393467 0.6665 0.6323 0.4734291 1.404803 0.0518841 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 16 500 5 nonlinear nonlinear 0.7040 0.4424435 0.4212745 0.0211689 0.5273770 0.4739102 1.374570 0.7828 0.5841 0.4773220 1.385228 0.0560475 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 17 500 5 nonlinear nonlinear 0.7500 0.4715649 0.4199045 0.0516604 0.5284281 0.4727296 1.390398 0.6290 0.6453 0.4694771 1.409677 0.0495726 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 18 500 5 nonlinear nonlinear 0.6430 0.4804614 0.4207486 0.0597128 0.5271993 0.4742212 1.405514 0.5785 0.5910 0.4790193 1.400314 0.0582708 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 19 500 5 nonlinear nonlinear 0.7395 0.4476996 0.4195283 0.0281713 0.5270491 0.4715681 1.384015 0.7610 0.5917 0.4772605 1.397464 0.0577322 21 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 20 500 5 nonlinear nonlinear 0.7345 0.4506150 0.4221543 0.0284607 0.5307777 0.4731930 1.370098 0.7648 0.5653 0.4838538 1.392100 0.0616995 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 21 500 5 nonlinear nonlinear 0.7455 0.4261188 0.4196305 0.0064884 0.5246498 0.4735568 1.360030 0.8967 0.5096 0.4882785 1.392985 0.0686481 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 22 500 5 nonlinear nonlinear 0.7325 0.4764428 0.4216920 0.0547509 0.5271259 0.4737313 1.406417 0.6026 0.5206 0.4864596 1.403520 0.0647677 20 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 23 500 5 nonlinear nonlinear 0.7515 0.4364392 0.4203214 0.0161178 0.5256286 0.4724089 1.374235 0.8159 0.6283 0.4708865 1.392750 0.0505651 19 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 24 500 5 nonlinear nonlinear 0.7265 0.4457707 0.4196404 0.0261303 0.5257699 0.4730944 1.372062 0.7732 0.6254 0.4722888 1.390067 0.0526484 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 25 500 5 nonlinear nonlinear 0.7285 0.5030288 0.4204915 0.0825374 0.5270996 0.4730752 1.405833 0.4863 0.5199 0.4937021 1.397364 0.0732106 20 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 26 500 5 nonlinear nonlinear 0.7340 0.4499834 0.4215227 0.0284607 0.5283968 0.4742735 1.364199 0.7902 0.5407 0.4890239 1.406400 0.0675012 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 27 500 5 nonlinear nonlinear 0.7310 0.4854062 0.4217148 0.0636914 0.5271850 0.4751048 1.396524 0.5974 0.6208 0.4763001 1.392531 0.0545853 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 28 500 5 nonlinear nonlinear 0.7105 0.4613558 0.4200258 0.0413300 0.5263629 0.4732697 1.388722 0.6982 0.5678 0.4824028 1.415244 0.0623770 22 0.2 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 29 500 5 nonlinear nonlinear 0.7150 0.4387846 0.4210916 0.0176930 0.5260463 0.4745317 1.376190 0.8167 0.5605 0.4815100 1.402867 0.0604184 23 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 30 500 5 nonlinear nonlinear 0.7425 0.4349089 0.4196735 0.0152355 0.5269741 0.4732100 1.364809 0.8357 0.5819 0.4783365 1.383467 0.0586631 19 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 31 500 5 nonlinear nonlinear 0.7365 0.4414452 0.4228662 0.0185790 0.5261762 0.4770133 1.372691 0.8097 0.5992 0.4773455 1.384692 0.0544793 22 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 32 500 5 nonlinear nonlinear 0.7205 0.4350263 0.4195394 0.0154869 0.5262428 0.4723581 1.377513 0.8233 0.5723 0.4767169 1.408838 0.0571775 19 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 33 500 5 nonlinear nonlinear 0.7670 0.4532668 0.4197906 0.0334763 0.5273395 0.4734985 1.377501 0.7040 0.6249 0.4716342 1.385131 0.0518437 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 34 500 5 nonlinear nonlinear 0.7215 0.4642611 0.4211446 0.0431165 0.5262374 0.4751447 1.387702 0.6606 0.5763 0.4806257 1.404799 0.0594811 22 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 35 500 5 nonlinear nonlinear 0.6975 0.4618276 0.4219323 0.0398953 0.5270493 0.4756455 1.393874 0.6881 0.5069 0.5016048 1.404933 0.0796725 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 36 500 5 nonlinear nonlinear 0.6895 0.4758253 0.4195300 0.0562953 0.5271729 0.4719910 1.377674 0.5744 0.5325 0.4827978 1.390354 0.0632678 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 37 500 5 nonlinear nonlinear 0.7325 0.4416460 0.4192615 0.0223845 0.5251625 0.4725419 1.372686 0.7797 0.5501 0.4844981 1.389755 0.0652366 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 38 500 5 nonlinear nonlinear 0.7235 0.4812122 0.4227259 0.0584863 0.5282750 0.4755733 1.422238 0.5583 0.5313 0.4917905 1.419110 0.0690647 21 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 39 500 5 nonlinear nonlinear 0.7355 0.4753797 0.4199046 0.0554751 0.5284799 0.4721401 1.384332 0.6012 0.5789 0.4772262 1.387512 0.0573216 21 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 40 500 5 nonlinear nonlinear 0.6585 0.4618269 0.4199687 0.0418582 0.5260643 0.4724010 1.367566 0.6533 0.5866 0.4758363 1.390088 0.0558676 24 0.4 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 41 500 5 nonlinear nonlinear 0.7040 0.4340599 0.4209447 0.0131152 0.5265937 0.4740688 1.373589 0.8396 0.5868 0.4804326 1.406190 0.0594879 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 42 500 5 nonlinear nonlinear 0.7345 0.4607202 0.4201337 0.0405866 0.5249497 0.4744721 1.394564 0.6934 0.5619 0.4803525 1.390632 0.0602188 20 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 43 500 5 nonlinear nonlinear 0.7540 0.4577152 0.4217131 0.0360021 0.5277782 0.4745331 1.383610 0.6965 0.5497 0.4832254 1.391134 0.0615123 17 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 44 500 5 nonlinear nonlinear 0.7030 0.4764057 0.4218110 0.0545947 0.5261217 0.4763988 1.396805 0.6022 0.5909 0.4792506 1.420235 0.0574396 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 45 500 5 nonlinear nonlinear 0.7260 0.4437981 0.4238533 0.0199449 0.5294330 0.4768053 1.372098 0.8007 0.6700 0.4679093 1.389681 0.0440560 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 46 500 5 nonlinear nonlinear 0.5890 0.4810828 0.4214519 0.0596308 0.5248801 0.4762623 1.415974 0.5688 0.5810 0.4794836 1.412049 0.0580317 20 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 47 500 5 nonlinear nonlinear 0.7700 0.4794666 0.4204114 0.0590552 0.5273504 0.4731108 1.386756 0.5523 0.6017 0.4749577 1.394664 0.0545463 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 48 500 5 nonlinear nonlinear 0.7315 0.4700487 0.4186075 0.0514412 0.5242963 0.4719231 1.375763 0.6621 0.5618 0.4769353 1.383087 0.0583278 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 49 500 5 nonlinear nonlinear 0.7365 0.4595509 0.4223251 0.0372258 0.5270149 0.4763277 1.372040 0.6806 0.4887 0.4939728 1.387554 0.0716477 25 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 50 500 5 nonlinear nonlinear 0.7190 0.4492186 0.4217198 0.0274988 0.5261532 0.4750597 1.371507 0.7853 0.6778 0.4653708 1.388778 0.0436510 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 51 500 5 nonlinear nonlinear 0.7350 0.4343783 0.4202302 0.0141482 0.5259282 0.4739619 1.364504 0.8331 0.6568 0.4657712 1.384071 0.0455410 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 52 500 5 nonlinear nonlinear 0.7330 0.4323976 0.4202113 0.0121863 0.5274514 0.4735158 1.367242 0.8430 0.5543 0.4816923 1.381731 0.0614810 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 53 500 5 nonlinear nonlinear 0.6230 0.4829459 0.4203151 0.0626309 0.5276125 0.4730780 1.410265 0.5887 0.5683 0.4838321 1.398561 0.0635170 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 54 500 5 nonlinear nonlinear 0.7100 0.4941922 0.4199798 0.0742124 0.5272073 0.4727327 1.380640 0.5532 0.5641 0.4817522 1.402555 0.0617724 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 55 500 5 nonlinear nonlinear 0.7065 0.4833707 0.4214088 0.0619619 0.5273577 0.4745520 1.411537 0.5779 0.5767 0.4816811 1.429976 0.0602724 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 56 500 5 nonlinear nonlinear 0.7345 0.4457100 0.4190474 0.0266625 0.5287796 0.4704816 1.366776 0.7653 0.5415 0.4866668 1.393438 0.0676193 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 57 500 5 nonlinear nonlinear 0.7275 0.4825077 0.4198046 0.0627031 0.5267406 0.4722389 1.396404 0.5570 0.6027 0.4741241 1.403317 0.0543195 25 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 58 500 5 nonlinear nonlinear 0.6960 0.4260222 0.4210298 0.0049925 0.5258627 0.4748687 1.369800 0.9076 0.5642 0.4761127 1.392275 0.0550829 21 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 59 500 5 nonlinear nonlinear 0.7235 0.4603972 0.4207295 0.0396677 0.5258456 0.4737882 1.399052 0.6841 0.5051 0.4966979 1.411552 0.0759684 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 60 500 5 nonlinear nonlinear 0.7420 0.4394016 0.4208486 0.0185530 0.5261849 0.4738387 1.361261 0.8024 0.5672 0.4797818 1.384341 0.0589332 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 61 500 5 nonlinear nonlinear 0.7425 0.4472879 0.4214068 0.0258812 0.5264004 0.4744889 1.372446 0.7815 0.6841 0.4641046 1.390245 0.0426978 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 62 500 5 nonlinear nonlinear 0.7155 0.4473952 0.4212965 0.0260986 0.5287223 0.4729912 1.362112 0.7497 0.5643 0.4794339 1.391191 0.0581374 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 63 500 5 nonlinear nonlinear 0.7665 0.5115404 0.4198376 0.0917029 0.5267781 0.4732034 1.393643 0.4901 0.4743 0.5074837 1.410710 0.0876461 21 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 64 500 5 nonlinear nonlinear 0.7450 0.4585254 0.4207177 0.0378077 0.5276881 0.4730779 1.383178 0.6871 0.5859 0.4770222 1.404315 0.0563044 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 65 500 5 nonlinear nonlinear 0.7370 0.4822271 0.4210131 0.0612140 0.5271401 0.4738928 1.400293 0.5726 0.5882 0.4807926 1.396735 0.0597796 19 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 66 500 5 nonlinear nonlinear 0.7490 0.4514892 0.4211194 0.0303698 0.5271176 0.4731806 1.368901 0.7342 0.5778 0.4779358 1.388338 0.0568165 19 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 67 500 5 nonlinear nonlinear 0.7270 0.4570978 0.4201261 0.0369717 0.5269936 0.4737052 1.375684 0.7143 0.5802 0.4791565 1.401172 0.0590304 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 68 500 5 nonlinear nonlinear 0.7565 0.4707917 0.4193674 0.0514244 0.5264491 0.4721899 1.381211 0.6155 0.5606 0.4810715 1.390502 0.0617041 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 69 500 5 nonlinear nonlinear 0.7425 0.4404451 0.4206451 0.0198000 0.5258873 0.4748553 1.365056 0.8047 0.5061 0.4956345 1.404671 0.0749893 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 70 500 5 nonlinear nonlinear 0.7360 0.4627163 0.4217550 0.0409613 0.5259538 0.4762693 1.376939 0.7009 0.7054 0.4607372 1.383509 0.0389822 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 71 500 5 nonlinear nonlinear 0.7045 0.4350637 0.4209919 0.0140718 0.5266992 0.4738271 1.364321 0.8334 0.5753 0.4795391 1.384602 0.0585471 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 72 500 5 nonlinear nonlinear 0.7465 0.4332618 0.4183436 0.0149183 0.5275548 0.4704761 1.364300 0.8442 0.5730 0.4807769 1.403876 0.0624333 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 73 500 5 nonlinear nonlinear 0.7110 0.5112242 0.4210294 0.0901949 0.5279174 0.4739353 1.386590 0.4590 0.5643 0.4810398 1.388980 0.0600105 20 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 74 500 5 nonlinear nonlinear 0.7370 0.4412950 0.4210172 0.0202779 0.5274912 0.4737883 1.379026 0.7965 0.6042 0.4750902 1.404139 0.0540731 24 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 75 500 5 nonlinear nonlinear 0.7115 0.4471793 0.4206634 0.0265160 0.5264456 0.4743948 1.374278 0.7701 0.5815 0.4779086 1.386233 0.0572453 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 76 500 5 nonlinear nonlinear 0.7325 0.4448171 0.4201640 0.0246532 0.5250591 0.4727443 1.368291 0.7698 0.5473 0.4906313 1.387069 0.0704673 21 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 77 500 5 nonlinear nonlinear 0.7540 0.4318959 0.4210730 0.0108229 0.5270405 0.4736845 1.368129 0.8574 0.5706 0.4787859 1.387842 0.0577129 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 78 500 5 nonlinear nonlinear 0.7450 0.4643003 0.4201939 0.0441064 0.5268612 0.4722790 1.387462 0.6779 0.5277 0.4844889 1.393274 0.0642951 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 79 500 5 nonlinear nonlinear 0.7485 0.4400852 0.4208514 0.0192338 0.5264551 0.4737965 1.361311 0.7926 0.5777 0.4786231 1.386648 0.0577716 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 80 500 5 nonlinear nonlinear 0.6975 0.4404574 0.4199664 0.0204910 0.5275030 0.4721625 1.395026 0.8057 0.6408 0.4709208 1.416417 0.0509544 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 81 500 5 nonlinear nonlinear 0.7190 0.4295729 0.4220693 0.0075036 0.5273375 0.4752285 1.367344 0.8874 0.5701 0.4803956 1.415482 0.0583263 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 82 500 5 nonlinear nonlinear 0.7400 0.4438037 0.4217338 0.0220699 0.5275131 0.4752874 1.373375 0.7809 0.5418 0.4825835 1.384653 0.0608497 23 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 83 500 5 nonlinear nonlinear 0.7050 0.4397946 0.4200888 0.0197058 0.5243915 0.4731500 1.383184 0.8071 0.5472 0.4818684 1.394695 0.0617796 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 84 500 5 nonlinear nonlinear 0.7400 0.4371791 0.4193313 0.0178478 0.5272684 0.4714282 1.352967 0.8077 0.6242 0.4723748 1.391515 0.0530435 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 85 500 5 nonlinear nonlinear 0.7505 0.4338149 0.4225038 0.0113111 0.5292094 0.4739221 1.373622 0.8522 0.5974 0.4775453 1.410121 0.0550415 21 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 86 500 5 nonlinear nonlinear 0.7120 0.4586530 0.4215308 0.0371221 0.5250063 0.4758721 1.374737 0.6941 0.5503 0.4868641 1.404617 0.0653332 21 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 87 500 5 nonlinear nonlinear 0.6705 0.4711477 0.4198224 0.0513253 0.5268288 0.4728729 1.407598 0.7166 0.4727 0.5039736 1.431031 0.0841512 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 88 500 5 nonlinear nonlinear 0.7215 0.4357414 0.4212938 0.0144476 0.5258961 0.4746987 1.360941 0.8358 0.5801 0.4787263 1.378360 0.0574324 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 89 500 5 nonlinear nonlinear 0.7245 0.4506369 0.4212039 0.0294330 0.5267670 0.4743235 1.370195 0.7319 0.6121 0.4743585 1.391535 0.0531546 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 90 500 5 nonlinear nonlinear 0.6980 0.4643087 0.4216492 0.0426595 0.5273716 0.4748278 1.390317 0.6562 0.5587 0.4820883 1.380750 0.0604390 23 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 91 500 5 nonlinear nonlinear 0.7185 0.4739634 0.4205244 0.0534390 0.5254962 0.4739634 1.369619 0.4316 0.5597 0.4776148 1.386268 0.0570904 20 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 92 500 5 nonlinear nonlinear 0.7460 0.4478824 0.4212700 0.0266124 0.5275300 0.4737229 1.370141 0.7461 0.5417 0.4885825 1.396064 0.0673125 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 93 500 5 nonlinear nonlinear 0.7695 0.4745776 0.4206113 0.0539662 0.5262493 0.4742048 1.378167 0.6195 0.5664 0.4775949 1.387852 0.0569836 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 94 500 5 nonlinear nonlinear 0.7285 0.4494503 0.4210675 0.0283828 0.5265986 0.4740031 1.368944 0.7447 0.5535 0.4856270 1.391138 0.0645595 22 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 95 500 5 nonlinear nonlinear 0.7570 0.4530546 0.4216735 0.0313811 0.5275289 0.4740873 1.377633 0.7351 0.6210 0.4751647 1.388227 0.0534912 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 96 500 5 nonlinear nonlinear 0.7195 0.4805496 0.4207109 0.0598387 0.5271014 0.4732749 1.389271 0.5847 0.5765 0.4807527 1.391342 0.0600418 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 97 500 5 nonlinear nonlinear 0.7300 0.4303188 0.4202657 0.0100530 0.5265658 0.4729083 1.374533 0.8753 0.6958 0.4604765 1.412853 0.0402107 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 98 500 5 nonlinear nonlinear 0.7125 0.5026115 0.4200842 0.0825274 0.5254531 0.4731033 1.381986 0.5493 0.4716 0.5084620 1.417306 0.0883779 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 99 500 5 nonlinear nonlinear 0.6660 0.4302575 0.4190340 0.0112234 0.5247917 0.4730965 1.373051 0.8568 0.5828 0.4772350 1.390708 0.0582010 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
19 100 500 5 nonlinear nonlinear 0.7255 0.4296779 0.4212597 0.0084182 0.5274310 0.4740792 1.378890 0.8776 0.5655 0.4798949 1.420641 0.0586352 22 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 1 1000 5 nonlinear nonlinear 0.7305 0.4420421 0.4229822 0.0190599 0.5278951 0.4760344 1.365646 0.8061 0.5877 0.4794886 1.385469 0.0565063 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 2 1000 5 nonlinear nonlinear 0.7420 0.4259549 0.4211886 0.0047663 0.5278487 0.4743903 1.357205 0.9110 0.6199 0.4747091 1.386735 0.0535205 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 3 1000 5 nonlinear nonlinear 0.7430 0.4446535 0.4210911 0.0235624 0.5261534 0.4746944 1.369138 0.7848 0.7695 0.4510493 1.383278 0.0299582 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 4 1000 5 nonlinear nonlinear 0.7500 0.4247019 0.4193868 0.0053150 0.5260981 0.4724023 1.353410 0.9029 0.5727 0.4781455 1.374234 0.0587586 28 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 5 1000 5 nonlinear nonlinear 0.7625 0.4401354 0.4200094 0.0201260 0.5254826 0.4741735 1.354664 0.8262 0.6357 0.4696823 1.382601 0.0496729 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 6 1000 5 nonlinear nonlinear 0.7200 0.4419665 0.4189243 0.0230422 0.5244153 0.4726885 1.371869 0.7779 0.5638 0.4781674 1.378900 0.0592431 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 7 1000 5 nonlinear nonlinear 0.7415 0.4342407 0.4196860 0.0145547 0.5254210 0.4732025 1.358575 0.8222 0.4994 0.4885070 1.379318 0.0688211 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 8 1000 5 nonlinear nonlinear 0.7310 0.4255883 0.4208796 0.0047086 0.5267800 0.4739460 1.365401 0.9092 0.6150 0.4736282 1.386051 0.0527485 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 9 1000 5 nonlinear nonlinear 0.6865 0.4262937 0.4208756 0.0054181 0.5267749 0.4747469 1.357945 0.9007 0.5185 0.4884322 1.378624 0.0675566 28 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 10 1000 5 nonlinear nonlinear 0.7390 0.4397739 0.4189017 0.0208723 0.5248298 0.4717748 1.369874 0.8219 0.5634 0.4776923 1.391544 0.0587906 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 11 1000 5 nonlinear nonlinear 0.7720 0.4334364 0.4217812 0.0116552 0.5272164 0.4749391 1.357611 0.8525 0.5805 0.4788920 1.377828 0.0571109 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 12 1000 5 nonlinear nonlinear 0.7175 0.4312753 0.4203785 0.0108969 0.5260039 0.4726242 1.361289 0.8489 0.5507 0.4794382 1.385982 0.0590597 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 13 1000 5 nonlinear nonlinear 0.7355 0.4760987 0.4214653 0.0546334 0.5271266 0.4742168 1.381969 0.6276 0.5879 0.4783516 1.394735 0.0568863 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 14 1000 5 nonlinear nonlinear 0.7705 0.4649616 0.4205180 0.0444436 0.5266376 0.4736199 1.386402 0.6759 0.5788 0.4778912 1.382650 0.0573732 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 15 1000 5 nonlinear nonlinear 0.7470 0.4231063 0.4199315 0.0031748 0.5251767 0.4740993 1.351430 0.9337 0.6069 0.4762008 1.387626 0.0562693 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 16 1000 5 nonlinear nonlinear 0.7455 0.4309420 0.4205508 0.0103913 0.5252875 0.4742886 1.358606 0.8577 0.6070 0.4740973 1.378037 0.0535466 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 17 1000 5 nonlinear nonlinear 0.7440 0.4383174 0.4198338 0.0184836 0.5277239 0.4727383 1.363855 0.8149 0.5688 0.4789750 1.383468 0.0591412 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 18 1000 5 nonlinear nonlinear 0.7465 0.4375153 0.4208346 0.0166807 0.5275499 0.4737675 1.374067 0.8297 0.5377 0.4850176 1.390186 0.0641831 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 19 1000 5 nonlinear nonlinear 0.6990 0.4319835 0.4213912 0.0105923 0.5263967 0.4749826 1.358525 0.8600 0.5539 0.4818485 1.383456 0.0604573 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 20 1000 5 nonlinear nonlinear 0.7550 0.4469526 0.4232772 0.0236754 0.5273986 0.4775509 1.353336 0.7759 0.5666 0.4814029 1.382001 0.0581256 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 21 1000 5 nonlinear nonlinear 0.7550 0.4412397 0.4206561 0.0205836 0.5271397 0.4747634 1.361185 0.7957 0.5839 0.4786899 1.380886 0.0580338 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 22 1000 5 nonlinear nonlinear 0.7540 0.4305718 0.4221555 0.0084163 0.5261872 0.4759142 1.354181 0.8731 0.5648 0.4806770 1.387847 0.0585215 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 23 1000 5 nonlinear nonlinear 0.7260 0.4398558 0.4215026 0.0183532 0.5267178 0.4748135 1.358006 0.8110 0.5742 0.4795897 1.388127 0.0580871 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 24 1000 5 nonlinear nonlinear 0.7440 0.4407541 0.4211064 0.0196477 0.5259036 0.4751099 1.370962 0.8065 0.7545 0.4536222 1.391053 0.0325158 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 25 1000 5 nonlinear nonlinear 0.7515 0.4528473 0.4201316 0.0327157 0.5260779 0.4732071 1.366051 0.7208 0.6028 0.4753024 1.381612 0.0551708 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 26 1000 5 nonlinear nonlinear 0.7095 0.4378970 0.4201530 0.0177440 0.5264686 0.4731019 1.363459 0.8145 0.5838 0.4771259 1.383038 0.0569729 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 27 1000 5 nonlinear nonlinear 0.7610 0.4388079 0.4193355 0.0194724 0.5253142 0.4719334 1.365296 0.8037 0.6018 0.4741294 1.381466 0.0547939 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 28 1000 5 nonlinear nonlinear 0.7450 0.4497947 0.4195274 0.0302673 0.5260392 0.4728107 1.381677 0.7345 0.5930 0.4770870 1.388647 0.0575596 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 29 1000 5 nonlinear nonlinear 0.7485 0.4761556 0.4214158 0.0547398 0.5284087 0.4738179 1.375440 0.6178 0.6302 0.4724669 1.373598 0.0510511 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 30 1000 5 nonlinear nonlinear 0.7390 0.4281910 0.4202988 0.0078922 0.5263088 0.4732528 1.358414 0.8801 0.5240 0.4912027 1.387549 0.0709039 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 31 1000 5 nonlinear nonlinear 0.7080 0.4539022 0.4213936 0.0325085 0.5264254 0.4743072 1.367495 0.7614 0.6504 0.4674871 1.385643 0.0460934 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 32 1000 5 nonlinear nonlinear 0.7400 0.4497131 0.4210177 0.0286955 0.5256752 0.4742251 1.376802 0.7398 0.5812 0.4780973 1.380553 0.0570796 26 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 33 1000 5 nonlinear nonlinear 0.7455 0.4341595 0.4210918 0.0130677 0.5291538 0.4733803 1.363315 0.8448 0.6616 0.4666940 1.382135 0.0456023 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 34 1000 5 nonlinear nonlinear 0.7635 0.4440054 0.4204814 0.0235240 0.5278244 0.4735136 1.359428 0.8331 0.5786 0.4788531 1.382738 0.0583716 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 35 1000 5 nonlinear nonlinear 0.7625 0.4285473 0.4219872 0.0065601 0.5258911 0.4762820 1.359815 0.8928 0.5982 0.4764547 1.383752 0.0544675 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 36 1000 5 nonlinear nonlinear 0.7390 0.4391745 0.4200452 0.0191293 0.5266357 0.4726515 1.362632 0.7957 0.5533 0.4813147 1.389762 0.0612694 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 37 1000 5 nonlinear nonlinear 0.6730 0.4645670 0.4224860 0.0420809 0.5278966 0.4757793 1.395436 0.6613 0.5548 0.4798918 1.406488 0.0574058 29 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 38 1000 5 nonlinear nonlinear 0.7320 0.4416721 0.4220921 0.0195800 0.5278835 0.4750342 1.366429 0.8008 0.5781 0.4801076 1.387595 0.0580155 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 39 1000 5 nonlinear nonlinear 0.7515 0.4936577 0.4213937 0.0722640 0.5259628 0.4759046 1.380695 0.5318 0.5670 0.4813628 1.382006 0.0599691 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 40 1000 5 nonlinear nonlinear 0.6985 0.4401920 0.4195209 0.0206711 0.5268774 0.4722437 1.366823 0.7959 0.6170 0.4714912 1.385538 0.0519704 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 41 1000 5 nonlinear nonlinear 0.7640 0.4741996 0.4209108 0.0532888 0.5282485 0.4725920 1.379043 0.6280 0.5862 0.4795009 1.395447 0.0585901 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 42 1000 5 nonlinear nonlinear 0.7620 0.4381358 0.4200597 0.0180761 0.5264157 0.4725869 1.371505 0.8183 0.7327 0.4541265 1.392860 0.0340668 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 43 1000 5 nonlinear nonlinear 0.7770 0.4334174 0.4215488 0.0118686 0.5277381 0.4747596 1.351467 0.8372 0.5204 0.4889722 1.377134 0.0674234 31 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 44 1000 5 nonlinear nonlinear 0.7310 0.4417856 0.4193579 0.0224277 0.5260433 0.4724576 1.365399 0.7895 0.5914 0.4753074 1.382739 0.0559495 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 45 1000 5 nonlinear nonlinear 0.7025 0.4720925 0.4191954 0.0528971 0.5253137 0.4722122 1.377420 0.5764 0.5341 0.4869544 1.394866 0.0677589 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 46 1000 5 nonlinear nonlinear 0.7440 0.4366408 0.4209181 0.0157228 0.5284446 0.4742494 1.360207 0.8265 0.5704 0.4796379 1.379812 0.0587198 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 47 1000 5 nonlinear nonlinear 0.7465 0.4356150 0.4202804 0.0153347 0.5268818 0.4732681 1.356910 0.8321 0.5726 0.4773766 1.380632 0.0570962 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 48 1000 5 nonlinear nonlinear 0.7715 0.4635107 0.4215781 0.0419326 0.5268859 0.4750790 1.364386 0.6975 0.5708 0.4803502 1.383569 0.0587721 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 49 1000 5 nonlinear nonlinear 0.7460 0.4433253 0.4193637 0.0239615 0.5275232 0.4721078 1.369690 0.7709 0.5532 0.4793221 1.382831 0.0599583 19 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 50 1000 5 nonlinear nonlinear 0.7525 0.4402597 0.4191447 0.0211150 0.5249735 0.4731265 1.356329 0.7981 0.5882 0.4760783 1.379471 0.0569337 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 51 1000 5 nonlinear nonlinear 0.7430 0.4250044 0.4213791 0.0036253 0.5268792 0.4745601 1.360514 0.9181 0.5924 0.4771792 1.388601 0.0558001 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 52 1000 5 nonlinear nonlinear 0.7490 0.4811917 0.4217763 0.0594154 0.5275941 0.4755087 1.380114 0.5760 0.6486 0.4691913 1.382465 0.0474150 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 53 1000 5 nonlinear nonlinear 0.7460 0.4305577 0.4214947 0.0090630 0.5278268 0.4738814 1.359885 0.8731 0.5242 0.4870009 1.383627 0.0655062 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 54 1000 5 nonlinear nonlinear 0.7430 0.4360850 0.4222746 0.0138104 0.5278136 0.4756834 1.364260 0.8334 0.5429 0.4851496 1.383535 0.0628750 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 55 1000 5 nonlinear nonlinear 0.7470 0.4225110 0.4189419 0.0035691 0.5248524 0.4728644 1.355751 0.9270 0.5817 0.4780959 1.383758 0.0591540 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 56 1000 5 nonlinear nonlinear 0.7730 0.4328981 0.4192296 0.0136684 0.5252902 0.4723955 1.359418 0.8371 0.7011 0.4583845 1.380308 0.0391548 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 57 1000 5 nonlinear nonlinear 0.7060 0.4440647 0.4211630 0.0229017 0.5287898 0.4733050 1.377297 0.8012 0.5661 0.4865493 1.387396 0.0653863 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 58 1000 5 nonlinear nonlinear 0.7225 0.4311026 0.4214429 0.0096598 0.5282176 0.4740414 1.361291 0.8664 0.5784 0.4797997 1.392696 0.0583568 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 59 1000 5 nonlinear nonlinear 0.7385 0.4430002 0.4205941 0.0224061 0.5284366 0.4730191 1.366445 0.7825 0.5790 0.4786439 1.381589 0.0580498 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 60 1000 5 nonlinear nonlinear 0.7375 0.4886836 0.4206809 0.0680026 0.5277365 0.4734255 1.371927 0.5076 0.5700 0.4784304 1.391478 0.0577495 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 61 1000 5 nonlinear nonlinear 0.7360 0.4274310 0.4185572 0.0088738 0.5282309 0.4703635 1.371967 0.8760 0.5736 0.4815567 1.385718 0.0629995 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 62 1000 5 nonlinear nonlinear 0.7430 0.4250122 0.4207265 0.0042857 0.5272235 0.4735691 1.349002 0.9147 0.5916 0.4763645 1.375610 0.0556380 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 63 1000 5 nonlinear nonlinear 0.7465 0.4443843 0.4200894 0.0242949 0.5248018 0.4743659 1.364748 0.7671 0.5616 0.4797038 1.382801 0.0596144 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 64 1000 5 nonlinear nonlinear 0.7585 0.4522921 0.4187639 0.0335281 0.5275474 0.4703845 1.368461 0.7451 0.5870 0.4757649 1.383360 0.0570010 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 65 1000 5 nonlinear nonlinear 0.7115 0.4321747 0.4192903 0.0128844 0.5256682 0.4728775 1.361776 0.8439 0.5705 0.4777345 1.383104 0.0584442 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 66 1000 5 nonlinear nonlinear 0.6535 0.4240237 0.4185745 0.0054493 0.5255553 0.4721786 1.366280 0.9062 0.6222 0.4702666 1.384658 0.0516922 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 67 1000 5 nonlinear nonlinear 0.7495 0.4294439 0.4214344 0.0080095 0.5261924 0.4753118 1.368178 0.8813 0.6347 0.4719286 1.387253 0.0504943 21 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 68 1000 5 nonlinear nonlinear 0.7235 0.4405640 0.4203158 0.0202482 0.5249790 0.4743086 1.372246 0.7971 0.4932 0.4910991 1.379659 0.0707834 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 69 1000 5 nonlinear nonlinear 0.7470 0.4722113 0.4202354 0.0519758 0.5282174 0.4723538 1.373756 0.6253 0.5270 0.4900149 1.382830 0.0697795 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 70 1000 5 nonlinear nonlinear 0.6815 0.4644858 0.4212853 0.0432005 0.5269111 0.4742371 1.383853 0.6617 0.5868 0.4775646 1.394216 0.0562793 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 71 1000 5 nonlinear nonlinear 0.7655 0.4653865 0.4210237 0.0443628 0.5276130 0.4744011 1.376035 0.6794 0.5694 0.4842867 1.393041 0.0632630 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 72 1000 5 nonlinear nonlinear 0.7545 0.4312187 0.4208073 0.0104114 0.5264294 0.4748294 1.359783 0.8662 0.5676 0.4802464 1.387994 0.0594392 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 73 1000 5 nonlinear nonlinear 0.7265 0.4493717 0.4198986 0.0294731 0.5264652 0.4727842 1.373054 0.7361 0.5865 0.4774017 1.385885 0.0575031 23 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 74 1000 5 nonlinear nonlinear 0.7690 0.4551173 0.4202411 0.0348762 0.5253841 0.4739248 1.364151 0.7150 0.5434 0.4852944 1.382629 0.0650533 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 75 1000 5 nonlinear nonlinear 0.7180 0.4278950 0.4205959 0.0072991 0.5258168 0.4740551 1.370742 0.8924 0.6197 0.4748965 1.392214 0.0543007 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 76 1000 5 nonlinear nonlinear 0.7685 0.4253564 0.4187351 0.0066214 0.5270336 0.4716280 1.359195 0.8853 0.5461 0.4850335 1.389768 0.0662985 28 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 77 1000 5 nonlinear nonlinear 0.7335 0.4849518 0.4201077 0.0648441 0.5259626 0.4727888 1.382961 0.5605 0.5766 0.4781205 1.388916 0.0580128 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 78 1000 5 nonlinear nonlinear 0.7095 0.4321741 0.4198779 0.0122962 0.5260733 0.4727235 1.362937 0.8470 0.5625 0.4798725 1.389930 0.0599946 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 79 1000 5 nonlinear nonlinear 0.6855 0.4442860 0.4214153 0.0228708 0.5277181 0.4747439 1.368950 0.7789 0.5484 0.4821049 1.388699 0.0606897 30 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 80 1000 5 nonlinear nonlinear 0.7570 0.4456558 0.4194341 0.0262218 0.5240102 0.4739483 1.367455 0.7516 0.5300 0.4847748 1.385059 0.0653408 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 81 1000 5 nonlinear nonlinear 0.7210 0.4608688 0.4223203 0.0385485 0.5295116 0.4745235 1.382627 0.7064 0.5899 0.4782888 1.388388 0.0559685 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 82 1000 5 nonlinear nonlinear 0.6850 0.4480367 0.4213905 0.0266463 0.5284045 0.4733809 1.364548 0.7430 0.5879 0.4773570 1.396351 0.0559666 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 83 1000 5 nonlinear nonlinear 0.7280 0.4320414 0.4190324 0.0130090 0.5258393 0.4726557 1.361583 0.8486 0.5964 0.4756377 1.385934 0.0566053 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 84 1000 5 nonlinear nonlinear 0.6745 0.4592546 0.4194433 0.0398114 0.5251900 0.4726323 1.367890 0.6961 0.5937 0.4750131 1.392495 0.0555698 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 85 1000 5 nonlinear nonlinear 0.7190 0.4589636 0.4227068 0.0362568 0.5284523 0.4750699 1.360669 0.6936 0.5640 0.4804616 1.380480 0.0577549 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 86 1000 5 nonlinear nonlinear 0.7400 0.4484779 0.4214703 0.0270076 0.5274940 0.4744980 1.373959 0.7695 0.6544 0.4684051 1.402909 0.0469348 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 87 1000 5 nonlinear nonlinear 0.7580 0.4438646 0.4207728 0.0230918 0.5270902 0.4737706 1.363565 0.7702 0.5625 0.4799834 1.386364 0.0592106 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 88 1000 5 nonlinear nonlinear 0.7695 0.4323853 0.4208565 0.0115288 0.5269306 0.4734823 1.371309 0.8559 0.6305 0.4708867 1.390813 0.0500303 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 89 1000 5 nonlinear nonlinear 0.7480 0.4462791 0.4205538 0.0257253 0.5263270 0.4738472 1.356268 0.7641 0.5637 0.4801576 1.381563 0.0596037 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 90 1000 5 nonlinear nonlinear 0.7415 0.4312374 0.4209699 0.0102675 0.5260360 0.4754734 1.366574 0.8611 0.5061 0.4881298 1.389228 0.0671599 27 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 91 1000 5 nonlinear nonlinear 0.7125 0.4359877 0.4209626 0.0150251 0.5265716 0.4740709 1.369753 0.8243 0.5586 0.4780296 1.388618 0.0570670 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 92 1000 5 nonlinear nonlinear 0.7255 0.4545530 0.4209830 0.0335700 0.5241987 0.4747333 1.375437 0.7607 0.5562 0.4934630 1.389516 0.0724801 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 93 1000 5 nonlinear nonlinear 0.7550 0.4287652 0.4231545 0.0056107 0.5287328 0.4759271 1.356755 0.9017 0.6100 0.4758839 1.375662 0.0527294 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 94 1000 5 nonlinear nonlinear 0.7480 0.4366049 0.4220594 0.0145455 0.5266466 0.4757267 1.359399 0.8135 0.6721 0.4661439 1.382779 0.0440846 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 95 1000 5 nonlinear nonlinear 0.7610 0.4310948 0.4209186 0.0101762 0.5279818 0.4734412 1.357422 0.8640 0.5435 0.4838871 1.378453 0.0629686 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 96 1000 5 nonlinear nonlinear 0.7590 0.4386461 0.4207607 0.0178854 0.5253997 0.4761269 1.359048 0.8158 0.6537 0.4682740 1.384298 0.0475133 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 97 1000 5 nonlinear nonlinear 0.7200 0.4561656 0.4207253 0.0354403 0.5291741 0.4729158 1.371756 0.7242 0.5773 0.4791571 1.380366 0.0584319 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 98 1000 5 nonlinear nonlinear 0.7040 0.4375390 0.4220396 0.0154994 0.5282243 0.4752791 1.370036 0.8443 0.6270 0.4735796 1.402167 0.0515400 23 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 99 1000 5 nonlinear nonlinear 0.7375 0.4357736 0.4218962 0.0138775 0.5280661 0.4744499 1.363561 0.8341 0.5848 0.4789269 1.381198 0.0570307 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
20 100 1000 5 nonlinear nonlinear 0.7320 0.4423006 0.4190669 0.0232337 0.5257637 0.4713921 1.360831 0.7731 0.4762 0.4912448 1.380528 0.0721778 28 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 1 2000 5 nonlinear nonlinear 0.7340 0.4379538 0.4229437 0.0150102 0.5285488 0.4759631 1.352737 0.8128 0.5700 0.4821991 1.376721 0.0592555 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 2 2000 5 nonlinear nonlinear 0.7355 0.4302895 0.4216170 0.0086725 0.5301403 0.4735924 1.357313 0.8789 0.6374 0.4713696 1.380156 0.0497526 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 3 2000 5 nonlinear nonlinear 0.7640 0.4300470 0.4226064 0.0074406 0.5257582 0.4770163 1.349628 0.8819 0.6285 0.4732252 1.376182 0.0506189 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 4 2000 5 nonlinear nonlinear 0.7150 0.4276073 0.4209623 0.0066449 0.5278523 0.4734158 1.350755 0.8915 0.6072 0.4749974 1.374410 0.0540351 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 5 2000 5 nonlinear nonlinear 0.7145 0.4277183 0.4205635 0.0071548 0.5260690 0.4738560 1.354364 0.8883 0.5194 0.4871577 1.376347 0.0665941 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 6 2000 5 nonlinear nonlinear 0.7095 0.4324771 0.4209004 0.0115767 0.5281791 0.4724069 1.356609 0.8522 0.5900 0.4761627 1.384105 0.0552623 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 7 2000 5 nonlinear nonlinear 0.7010 0.4233127 0.4205335 0.0027793 0.5263122 0.4734515 1.358231 0.9247 0.5704 0.4744279 1.380645 0.0538945 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 8 2000 5 nonlinear nonlinear 0.7510 0.4322367 0.4209702 0.0112666 0.5272936 0.4736095 1.366787 0.8588 0.6852 0.4624739 1.384563 0.0415037 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 9 2000 5 nonlinear nonlinear 0.7170 0.4384244 0.4218706 0.0165538 0.5269607 0.4753617 1.353692 0.8400 0.5704 0.4812328 1.374977 0.0593623 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 10 2000 5 nonlinear nonlinear 0.7630 0.4294379 0.4212957 0.0081422 0.5265366 0.4752470 1.357381 0.8834 0.5997 0.4759446 1.378950 0.0546489 38 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 11 2000 5 nonlinear nonlinear 0.7480 0.4256493 0.4204726 0.0051767 0.5258775 0.4741426 1.356089 0.9044 0.5105 0.4880349 1.378383 0.0675624 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 12 2000 5 nonlinear nonlinear 0.7555 0.4252414 0.4203127 0.0049286 0.5269415 0.4733868 1.361327 0.9085 0.5879 0.4768868 1.384313 0.0565741 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 13 2000 5 nonlinear nonlinear 0.7515 0.4534149 0.4200554 0.0333595 0.5262833 0.4733731 1.368419 0.7101 0.5117 0.4879522 1.374261 0.0678968 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 14 2000 5 nonlinear nonlinear 0.7560 0.4238204 0.4206792 0.0031412 0.5249169 0.4744045 1.358876 0.9253 0.5515 0.4827629 1.376265 0.0620837 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 15 2000 5 nonlinear nonlinear 0.7555 0.4318710 0.4204249 0.0114462 0.5267574 0.4738293 1.358193 0.8548 0.6514 0.4687280 1.379576 0.0483031 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 16 2000 5 nonlinear nonlinear 0.7730 0.4281813 0.4212277 0.0069536 0.5273043 0.4737912 1.357425 0.8966 0.6678 0.4652449 1.383197 0.0440171 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 17 2000 5 nonlinear nonlinear 0.7205 0.4233084 0.4206853 0.0026231 0.5257043 0.4739124 1.351795 0.9305 0.5907 0.4765594 1.378528 0.0558741 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 18 2000 5 nonlinear nonlinear 0.7785 0.4278867 0.4196841 0.0082026 0.5265382 0.4725298 1.354642 0.8803 0.5630 0.4793018 1.378564 0.0596176 34 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 19 2000 5 nonlinear nonlinear 0.7835 0.4276115 0.4209402 0.0066713 0.5257957 0.4750205 1.359586 0.8888 0.5954 0.4762821 1.376039 0.0553419 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 20 2000 5 nonlinear nonlinear 0.7285 0.4379791 0.4207367 0.0172424 0.5242498 0.4758987 1.363783 0.8133 0.5022 0.4900340 1.378732 0.0692973 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 21 2000 5 nonlinear nonlinear 0.7485 0.4299842 0.4212649 0.0087193 0.5277133 0.4742619 1.359979 0.8753 0.5924 0.4780002 1.379285 0.0567354 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 22 2000 5 nonlinear nonlinear 0.7565 0.4310392 0.4205057 0.0105335 0.5265496 0.4734064 1.362226 0.8619 0.6894 0.4610863 1.382707 0.0405806 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 23 2000 5 nonlinear nonlinear 0.7605 0.4277688 0.4206616 0.0071072 0.5282531 0.4730543 1.362194 0.8910 0.5771 0.4781710 1.379874 0.0575094 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 24 2000 5 nonlinear nonlinear 0.7710 0.4233774 0.4200050 0.0033724 0.5238564 0.4742041 1.354605 0.9239 0.5658 0.4790733 1.376889 0.0590683 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 25 2000 5 nonlinear nonlinear 0.7330 0.4359793 0.4196909 0.0162884 0.5248044 0.4735977 1.361173 0.8233 0.6343 0.4696908 1.374309 0.0499999 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 26 2000 5 nonlinear nonlinear 0.7550 0.4212668 0.4194804 0.0017864 0.5258117 0.4717819 1.353752 0.9489 0.5731 0.4783762 1.378521 0.0588958 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 27 2000 5 nonlinear nonlinear 0.7610 0.4350775 0.4205847 0.0144928 0.5278548 0.4733596 1.364394 0.8343 0.5671 0.4801067 1.380824 0.0595220 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 28 2000 5 nonlinear nonlinear 0.7670 0.4325946 0.4211002 0.0114944 0.5278366 0.4743231 1.349079 0.8514 0.6415 0.4691933 1.381617 0.0480931 31 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 29 2000 5 nonlinear nonlinear 0.6995 0.4312358 0.4211180 0.0101178 0.5272063 0.4746187 1.356626 0.8598 0.5623 0.4776045 1.379626 0.0564865 24 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 30 2000 5 nonlinear nonlinear 0.7055 0.4459696 0.4218233 0.0241463 0.5261220 0.4761455 1.353992 0.7705 0.6074 0.4748353 1.377440 0.0530120 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 31 2000 5 nonlinear nonlinear 0.7595 0.4279897 0.4207900 0.0071996 0.5260389 0.4737365 1.358820 0.8671 0.6333 0.4704767 1.381075 0.0496866 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 32 2000 5 nonlinear nonlinear 0.7010 0.4277178 0.4201314 0.0075864 0.5263255 0.4733080 1.346004 0.8938 0.5999 0.4748427 1.376042 0.0547112 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 33 2000 5 nonlinear nonlinear 0.7495 0.4276938 0.4216717 0.0060221 0.5270013 0.4754482 1.356233 0.8974 0.5975 0.4769010 1.378425 0.0552292 30 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 34 2000 5 nonlinear nonlinear 0.7455 0.4236014 0.4190568 0.0045446 0.5248467 0.4723559 1.353663 0.9128 0.5867 0.4754314 1.375921 0.0563746 31 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 35 2000 5 nonlinear nonlinear 0.7395 0.4232877 0.4196531 0.0036346 0.5257369 0.4732170 1.357788 0.9206 0.5605 0.4807226 1.378789 0.0610696 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 36 2000 5 nonlinear nonlinear 0.7005 0.4308480 0.4212316 0.0096165 0.5275026 0.4741830 1.354510 0.8639 0.5772 0.4793388 1.373711 0.0581072 35 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 37 2000 5 nonlinear nonlinear 0.7550 0.4338912 0.4215860 0.0123052 0.5267156 0.4747026 1.354005 0.8435 0.5768 0.4788322 1.375281 0.0572462 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 38 2000 5 nonlinear nonlinear 0.7570 0.4308480 0.4206595 0.0101885 0.5251233 0.4746404 1.358569 0.8590 0.5521 0.4821252 1.384786 0.0614657 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 39 2000 5 nonlinear nonlinear 0.7245 0.4275678 0.4224561 0.0051118 0.5268362 0.4757927 1.357053 0.9040 0.5920 0.4788899 1.377061 0.0564339 37 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 40 2000 5 nonlinear nonlinear 0.7555 0.4320708 0.4193688 0.0127020 0.5268215 0.4723847 1.362470 0.8822 0.5816 0.4793008 1.383202 0.0599320 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 41 2000 5 nonlinear nonlinear 0.7575 0.4315569 0.4215697 0.0099872 0.5271133 0.4749306 1.352687 0.8674 0.6028 0.4760701 1.377608 0.0545004 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 42 2000 5 nonlinear nonlinear 0.7665 0.4292289 0.4205864 0.0086425 0.5261029 0.4739624 1.354704 0.8763 0.6037 0.4745514 1.378186 0.0539650 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 43 2000 5 nonlinear nonlinear 0.6970 0.4368272 0.4193810 0.0174462 0.5251294 0.4732170 1.357485 0.8112 0.6012 0.4747135 1.377375 0.0553325 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 44 2000 5 nonlinear nonlinear 0.7365 0.4294840 0.4204987 0.0089853 0.5273853 0.4738833 1.351918 0.8780 0.5639 0.4805787 1.372100 0.0600800 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 45 2000 5 nonlinear nonlinear 0.7390 0.4333137 0.4203231 0.0129906 0.5239605 0.4749122 1.359321 0.8439 0.5545 0.4814116 1.378298 0.0610885 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 46 2000 5 nonlinear nonlinear 0.6890 0.4227520 0.4186488 0.0041032 0.5229427 0.4729166 1.355319 0.9168 0.5115 0.4861246 1.377794 0.0674757 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 47 2000 5 nonlinear nonlinear 0.7435 0.4230316 0.4191234 0.0039083 0.5280739 0.4706480 1.354145 0.9184 0.5760 0.4768493 1.380301 0.0577260 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 48 2000 5 nonlinear nonlinear 0.7365 0.4343716 0.4213098 0.0130618 0.5263548 0.4742519 1.357462 0.8403 0.4822 0.4934747 1.373236 0.0721649 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 49 2000 5 nonlinear nonlinear 0.7675 0.4257754 0.4218414 0.0039340 0.5267501 0.4755089 1.351293 0.9339 0.5647 0.4815296 1.373773 0.0596882 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 50 2000 5 nonlinear nonlinear 0.7545 0.4358654 0.4204747 0.0153907 0.5262087 0.4729194 1.361499 0.8290 0.5139 0.4865766 1.380454 0.0661019 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 51 2000 5 nonlinear nonlinear 0.7230 0.4330175 0.4194736 0.0135439 0.5249285 0.4726765 1.364281 0.8400 0.4918 0.4901612 1.382589 0.0706876 31 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 52 2000 5 nonlinear nonlinear 0.7400 0.4469178 0.4198280 0.0270897 0.5266261 0.4715111 1.361614 0.7490 0.5770 0.4757632 1.379010 0.0559352 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 53 2000 5 nonlinear nonlinear 0.7460 0.4284722 0.4195278 0.0089444 0.5272182 0.4715991 1.358679 0.8794 0.6262 0.4707083 1.377418 0.0511804 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 54 2000 5 nonlinear nonlinear 0.7620 0.4272123 0.4214044 0.0058079 0.5253361 0.4750912 1.354519 0.8956 0.5563 0.4815067 1.384101 0.0601023 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 55 2000 5 nonlinear nonlinear 0.7540 0.4313224 0.4225173 0.0088051 0.5270001 0.4763253 1.350114 0.8716 0.5978 0.4776549 1.371854 0.0551376 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 56 2000 5 nonlinear nonlinear 0.7730 0.4219477 0.4193498 0.0025979 0.5267595 0.4710224 1.352431 0.9299 0.5639 0.4775464 1.376792 0.0581966 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 57 2000 5 nonlinear nonlinear 0.7580 0.4356937 0.4205062 0.0151876 0.5251352 0.4745076 1.357720 0.8235 0.6308 0.4708435 1.378910 0.0503373 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 58 2000 5 nonlinear nonlinear 0.7505 0.4237285 0.4197172 0.0040114 0.5244206 0.4730214 1.357203 0.9172 0.4609 0.4938912 1.376143 0.0741741 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 59 2000 5 nonlinear nonlinear 0.7480 0.4305374 0.4221180 0.0084194 0.5276778 0.4758414 1.361459 0.8739 0.5293 0.4875648 1.385744 0.0654468 39 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 60 2000 5 nonlinear nonlinear 0.7335 0.4259349 0.4226679 0.0032669 0.5273975 0.4758810 1.351016 0.9230 0.6450 0.4706245 1.375622 0.0479565 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 61 2000 5 nonlinear nonlinear 0.7540 0.4335748 0.4196491 0.0139257 0.5255568 0.4730797 1.362790 0.8396 0.5817 0.4769983 1.378939 0.0573491 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 62 2000 5 nonlinear nonlinear 0.7515 0.4260173 0.4209284 0.0050889 0.5247080 0.4746950 1.355821 0.8997 0.5368 0.4838080 1.375512 0.0628796 30 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 63 2000 5 nonlinear nonlinear 0.7410 0.4277800 0.4217853 0.0059947 0.5275266 0.4741615 1.351099 0.8903 0.6142 0.4732368 1.375133 0.0514515 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 64 2000 5 nonlinear nonlinear 0.7250 0.4218797 0.4204650 0.0014146 0.5264606 0.4736493 1.351905 0.9438 0.5515 0.4812089 1.377154 0.0607439 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 65 2000 5 nonlinear nonlinear 0.7060 0.4312529 0.4196913 0.0115615 0.5259681 0.4727798 1.360576 0.8608 0.4640 0.4937503 1.379025 0.0740589 34 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 66 2000 5 nonlinear nonlinear 0.7645 0.4336343 0.4204454 0.0131889 0.5250665 0.4741766 1.351185 0.8397 0.4872 0.4921105 1.372413 0.0716651 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 67 2000 5 nonlinear nonlinear 0.7715 0.4244868 0.4227615 0.0017253 0.5272343 0.4753233 1.355592 0.9427 0.6202 0.4734848 1.379541 0.0507233 31 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 68 2000 5 nonlinear nonlinear 0.7470 0.4446587 0.4202718 0.0243869 0.5257921 0.4741941 1.353465 0.8261 0.5505 0.4826918 1.376537 0.0624200 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 69 2000 5 nonlinear nonlinear 0.7500 0.4318953 0.4216542 0.0102411 0.5273895 0.4743633 1.355501 0.8702 0.6069 0.4751578 1.382168 0.0535036 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 70 2000 5 nonlinear nonlinear 0.7495 0.4275853 0.4224718 0.0051135 0.5258209 0.4756659 1.354451 0.8968 0.5193 0.4871084 1.378534 0.0646365 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 71 2000 5 nonlinear nonlinear 0.7215 0.4326052 0.4203107 0.0122946 0.5268490 0.4738652 1.353073 0.8645 0.6101 0.4736612 1.377863 0.0533505 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 72 2000 5 nonlinear nonlinear 0.7245 0.4377546 0.4200401 0.0177144 0.5259123 0.4734818 1.363600 0.8171 0.5872 0.4767328 1.382322 0.0566927 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 73 2000 5 nonlinear nonlinear 0.7525 0.4204459 0.4193908 0.0010551 0.5251754 0.4732936 1.353074 0.9567 0.5741 0.4781761 1.372221 0.0587853 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 74 2000 5 nonlinear nonlinear 0.6965 0.4238197 0.4199680 0.0038517 0.5264013 0.4725280 1.354662 0.9175 0.5554 0.4810398 1.372681 0.0610718 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 75 2000 5 nonlinear nonlinear 0.7640 0.4237854 0.4213344 0.0024510 0.5267799 0.4736510 1.354283 0.9239 0.5888 0.4766334 1.381384 0.0552989 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 76 2000 5 nonlinear nonlinear 0.7090 0.4240206 0.4191520 0.0048686 0.5266784 0.4720281 1.352953 0.9107 0.6157 0.4711076 1.369052 0.0519556 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 77 2000 5 nonlinear nonlinear 0.7770 0.4251051 0.4197075 0.0053975 0.5240047 0.4737392 1.357891 0.9006 0.5771 0.4768774 1.382446 0.0571699 30 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 78 2000 5 nonlinear nonlinear 0.7435 0.4241457 0.4208623 0.0032834 0.5269362 0.4741877 1.359199 0.9272 0.6392 0.4697993 1.378583 0.0489370 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 79 2000 5 nonlinear nonlinear 0.7695 0.4305317 0.4214353 0.0090964 0.5256984 0.4755641 1.355312 0.8680 0.5665 0.4807820 1.377588 0.0593467 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 80 2000 5 nonlinear nonlinear 0.7225 0.4379623 0.4208246 0.0171377 0.5261912 0.4739422 1.354594 0.8159 0.5628 0.4828635 1.380359 0.0620389 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 81 2000 5 nonlinear nonlinear 0.7450 0.4278877 0.4224633 0.0054244 0.5278651 0.4756218 1.346894 0.9003 0.5545 0.4842169 1.374120 0.0617536 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 82 2000 5 nonlinear nonlinear 0.7750 0.4239875 0.4196896 0.0042980 0.5274712 0.4722637 1.351705 0.9106 0.5580 0.4807482 1.372905 0.0610587 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 83 2000 5 nonlinear nonlinear 0.7400 0.4300619 0.4215869 0.0084750 0.5257576 0.4761353 1.356102 0.8739 0.5045 0.4916182 1.377396 0.0700313 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 84 2000 5 nonlinear nonlinear 0.7220 0.4277468 0.4189735 0.0087733 0.5259910 0.4721663 1.357616 0.8742 0.5702 0.4780895 1.376437 0.0591161 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 85 2000 5 nonlinear nonlinear 0.7570 0.4347760 0.4207036 0.0140724 0.5271025 0.4725685 1.353264 0.8326 0.6140 0.4734888 1.378659 0.0527852 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 86 2000 5 nonlinear nonlinear 0.7095 0.4224297 0.4212751 0.0011546 0.5271869 0.4739745 1.358217 0.9538 0.5620 0.4816240 1.381234 0.0603490 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 87 2000 5 nonlinear nonlinear 0.7590 0.4283992 0.4220488 0.0063504 0.5257905 0.4765875 1.360999 0.8947 0.6218 0.4743875 1.381641 0.0523386 37 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 88 2000 5 nonlinear nonlinear 0.7995 0.4312587 0.4206955 0.0105631 0.5264113 0.4737655 1.359670 0.8650 0.5672 0.4800885 1.386172 0.0593930 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 89 2000 5 nonlinear nonlinear 0.7530 0.4244768 0.4195318 0.0049450 0.5243698 0.4736636 1.354878 0.9075 0.5904 0.4768867 1.380719 0.0573550 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 90 2000 5 nonlinear nonlinear 0.6430 0.4274037 0.4205307 0.0068729 0.5261267 0.4737742 1.359291 0.8911 0.5777 0.4777830 1.378788 0.0572523 38 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 91 2000 5 nonlinear nonlinear 0.7170 0.4243851 0.4215268 0.0028583 0.5280520 0.4740896 1.353857 0.9315 0.6172 0.4732936 1.375228 0.0517668 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 92 2000 5 nonlinear nonlinear 0.7305 0.4346475 0.4206655 0.0139821 0.5248790 0.4745044 1.362838 0.8378 0.5603 0.4798193 1.376222 0.0591538 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 93 2000 5 nonlinear nonlinear 0.7355 0.4290567 0.4234511 0.0056056 0.5274230 0.4770227 1.354175 0.9028 0.5508 0.4845169 1.378501 0.0610658 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 94 2000 5 nonlinear nonlinear 0.7695 0.4297840 0.4218874 0.0078967 0.5261116 0.4762023 1.357634 0.8707 0.6065 0.4756443 1.379083 0.0537569 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 95 2000 5 nonlinear nonlinear 0.7370 0.4282688 0.4198693 0.0083995 0.5267887 0.4722619 1.361299 0.8783 0.5251 0.4871494 1.384875 0.0672801 28 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 96 2000 5 nonlinear nonlinear 0.7670 0.4385694 0.4232458 0.0153236 0.5282698 0.4759284 1.356737 0.8255 0.5703 0.4816746 1.376998 0.0584288 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 97 2000 5 nonlinear nonlinear 0.7750 0.4250097 0.4211081 0.0039016 0.5259642 0.4750307 1.351981 0.9148 0.5756 0.4793659 1.378057 0.0582578 37 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 98 2000 5 nonlinear nonlinear 0.7280 0.4336236 0.4199452 0.0136784 0.5273098 0.4730975 1.357460 0.8421 0.5379 0.4841061 1.378119 0.0641609 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 99 2000 5 nonlinear nonlinear 0.7535 0.4322423 0.4210974 0.0111449 0.5271179 0.4739997 1.356301 0.8579 0.5770 0.4787493 1.377053 0.0576519 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
21 100 2000 5 nonlinear nonlinear 0.7475 0.4332710 0.4200141 0.0132569 0.5258276 0.4736537 1.357818 0.8404 0.5751 0.4776228 1.376803 0.0576087 30 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
22 1 500 10 nonlinear nonlinear 0.5535 0.4634544 0.4192769 0.0441775 0.5272960 0.4715690 1.411965 0.6732 0.5756 0.4808633 1.412966 0.0615865 25 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 2 500 10 nonlinear nonlinear 0.5960 0.4485714 0.4212922 0.0272792 0.5253913 0.4747205 1.371329 0.7592 0.6265 0.4727922 1.407974 0.0515000 24 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 3 500 10 nonlinear nonlinear 0.5905 0.4578519 0.4226234 0.0352285 0.5284785 0.4755360 1.395123 0.7269 0.5789 0.4804928 1.418329 0.0578694 26 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 4 500 10 nonlinear nonlinear 0.5540 0.4930075 0.4213753 0.0716322 0.5274123 0.4743019 1.416230 0.5212 0.5691 0.4788279 1.416215 0.0574526 31 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 5 500 10 nonlinear nonlinear 0.5820 0.4397109 0.4202520 0.0194589 0.5270481 0.4723118 1.378459 0.8060 0.5785 0.4822817 1.420740 0.0620297 25 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 6 500 10 nonlinear nonlinear 0.5595 0.4699389 0.4209148 0.0490241 0.5286384 0.4733487 1.406721 0.6362 0.5468 0.4914986 1.422375 0.0705838 22 0.8 1.0 1.0 0.5714286 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 7 500 10 nonlinear nonlinear 0.4875 0.4793380 0.4198937 0.0594443 0.5266642 0.4720780 1.416255 0.5979 0.5755 0.4820714 1.406222 0.0621777 28 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 8 500 10 nonlinear nonlinear 0.5535 0.4803699 0.4198273 0.0605426 0.5279676 0.4726144 1.399296 0.5603 0.5925 0.4761599 1.419791 0.0563326 22 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 9 500 10 nonlinear nonlinear 0.5585 0.4895983 0.4202779 0.0693204 0.5249079 0.4733519 1.416360 0.5304 0.5356 0.4925802 1.432938 0.0723023 24 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 10 500 10 nonlinear nonlinear 0.6095 0.4683200 0.4215817 0.0467383 0.5292377 0.4729216 1.381441 0.6610 0.6099 0.4750903 1.410683 0.0535085 26 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 11 500 10 nonlinear nonlinear 0.5105 0.4767651 0.4204403 0.0563248 0.5283646 0.4732321 1.397292 0.6218 0.5647 0.4843363 1.417392 0.0638961 24 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 12 500 10 nonlinear nonlinear 0.5655 0.4950488 0.4206029 0.0744459 0.5273900 0.4734201 1.388491 0.5211 0.5828 0.4802568 1.402311 0.0596539 25 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 13 500 10 nonlinear nonlinear 0.5635 0.4657332 0.4212455 0.0444878 0.5271613 0.4745694 1.410929 0.6664 0.6345 0.4721331 1.441576 0.0508876 27 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 14 500 10 nonlinear nonlinear 0.5665 0.4790511 0.4213859 0.0576652 0.5286994 0.4725357 1.386868 0.5501 0.5749 0.4786215 1.395706 0.0572356 29 0.6 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 15 500 10 nonlinear nonlinear 0.5460 0.4401805 0.4197476 0.0204329 0.5262494 0.4718026 1.378233 0.7977 0.5799 0.4765805 1.401472 0.0568329 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 16 500 10 nonlinear nonlinear 0.5875 0.4765607 0.4189441 0.0576167 0.5254770 0.4714938 1.404554 0.6196 0.6023 0.4759378 1.426116 0.0569938 25 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 17 500 10 nonlinear nonlinear 0.5890 0.4621892 0.4217913 0.0403978 0.5273743 0.4751364 1.391728 0.7457 0.6396 0.4708454 1.413387 0.0490540 24 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 18 500 10 nonlinear nonlinear 0.6020 0.4839114 0.4202192 0.0636921 0.5278272 0.4730137 1.388359 0.5645 0.5513 0.4842667 1.421078 0.0640475 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 19 500 10 nonlinear nonlinear 0.5325 0.4582905 0.4199426 0.0383478 0.5260855 0.4736048 1.402106 0.6777 0.5789 0.4781650 1.418060 0.0582224 26 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 20 500 10 nonlinear nonlinear 0.4635 0.4715275 0.4198698 0.0516578 0.5256250 0.4730371 1.399319 0.5896 0.5618 0.4788829 1.406341 0.0590131 28 0.4 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 21 500 10 nonlinear nonlinear 0.5065 0.5165071 0.4212668 0.0952403 0.5267393 0.4747225 1.420978 0.4553 0.6062 0.4758432 1.413422 0.0545764 30 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 22 500 10 nonlinear nonlinear 0.5370 0.4660075 0.4205795 0.0454280 0.5270971 0.4733988 1.413007 0.6598 0.5559 0.4837054 1.439083 0.0631260 23 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 23 500 10 nonlinear nonlinear 0.5750 0.4936700 0.4215590 0.0721111 0.5279748 0.4750366 1.410436 0.5325 0.5258 0.4934381 1.430038 0.0718791 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 24 500 10 nonlinear nonlinear 0.6080 0.4644669 0.4211481 0.0433188 0.5272677 0.4741676 1.375109 0.6842 0.5446 0.4888849 1.410847 0.0677367 19 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 25 500 10 nonlinear nonlinear 0.6105 0.4549406 0.4216428 0.0332979 0.5272937 0.4755848 1.367735 0.7497 0.6589 0.4686350 1.401189 0.0469922 28 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 26 500 10 nonlinear nonlinear 0.5810 0.4750144 0.4193174 0.0556969 0.5260094 0.4716501 1.401850 0.5663 0.5654 0.4801693 1.420923 0.0608519 28 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 27 500 10 nonlinear nonlinear 0.6020 0.4652126 0.4211302 0.0440824 0.5263336 0.4741280 1.384207 0.6631 0.5715 0.4789684 1.390124 0.0578382 22 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 28 500 10 nonlinear nonlinear 0.5930 0.4282180 0.4203028 0.0079152 0.5259003 0.4735008 1.376898 0.8814 0.5421 0.4869931 1.443060 0.0666903 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 29 500 10 nonlinear nonlinear 0.5945 0.4925651 0.4220513 0.0705138 0.5273690 0.4745432 1.391764 0.5516 0.6237 0.4747463 1.400406 0.0526950 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 30 500 10 nonlinear nonlinear 0.5560 0.4744650 0.4207515 0.0537135 0.5261478 0.4740597 1.403733 0.5705 0.5872 0.4787202 1.410383 0.0579687 25 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 31 500 10 nonlinear nonlinear 0.5410 0.4467526 0.4200247 0.0267279 0.5248319 0.4735948 1.393517 0.7526 0.5593 0.4799845 1.426910 0.0599598 31 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 32 500 10 nonlinear nonlinear 0.5200 0.5215277 0.4211770 0.1003508 0.5255827 0.4749187 1.437360 0.4513 0.5225 0.5019539 1.462065 0.0807769 28 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 33 500 10 nonlinear nonlinear 0.5425 0.4772785 0.4197182 0.0575603 0.5269892 0.4723640 1.403475 0.5575 0.5762 0.4785899 1.423497 0.0588716 28 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 34 500 10 nonlinear nonlinear 0.5200 0.4548757 0.4216755 0.0332001 0.5283526 0.4756454 1.398498 0.7146 0.5559 0.4830079 1.431188 0.0613323 29 0.6 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 35 500 10 nonlinear nonlinear 0.5495 0.4983981 0.4203485 0.0780496 0.5279092 0.4727334 1.399717 0.4615 0.5564 0.4843179 1.428307 0.0639695 26 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 36 500 10 nonlinear nonlinear 0.5240 0.4741616 0.4215562 0.0526054 0.5268655 0.4747597 1.408580 0.5880 0.6272 0.4727411 1.417315 0.0511849 26 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 37 500 10 nonlinear nonlinear 0.5585 0.4900546 0.4213999 0.0686547 0.5255992 0.4749451 1.425763 0.5581 0.5095 0.4996175 1.432005 0.0782177 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 38 500 10 nonlinear nonlinear 0.3910 0.4654783 0.4201908 0.0452875 0.5258531 0.4731104 1.440173 0.6346 0.5699 0.4791803 1.408694 0.0589895 25 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 39 500 10 nonlinear nonlinear 0.5840 0.4957273 0.4211420 0.0745854 0.5269916 0.4740968 1.412560 0.5137 0.5490 0.4838745 1.415764 0.0627325 23 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 40 500 10 nonlinear nonlinear 0.5415 0.4768354 0.4212299 0.0556055 0.5261594 0.4753063 1.410409 0.6021 0.5671 0.4794816 1.432875 0.0582517 26 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 41 500 10 nonlinear nonlinear 0.5475 0.4965341 0.4204257 0.0761084 0.5261895 0.4735421 1.418683 0.5247 0.5718 0.4814694 1.425768 0.0610436 23 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 42 500 10 nonlinear nonlinear 0.5830 0.4745722 0.4205412 0.0540310 0.5261163 0.4739771 1.407839 0.5659 0.5778 0.4826363 1.442710 0.0620951 25 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 43 500 10 nonlinear nonlinear 0.5360 0.4792428 0.4215604 0.0576824 0.5264604 0.4746979 1.413883 0.5695 0.5709 0.4794886 1.433204 0.0579282 25 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 44 500 10 nonlinear nonlinear 0.5480 0.4503802 0.4197062 0.0306739 0.5282107 0.4718434 1.372802 0.7323 0.5635 0.4838795 1.435856 0.0641733 24 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 45 500 10 nonlinear nonlinear 0.5975 0.4389384 0.4209664 0.0179719 0.5274791 0.4738139 1.362300 0.8124 0.6649 0.4663754 1.401844 0.0454090 24 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 46 500 10 nonlinear nonlinear 0.5585 0.4617617 0.4197832 0.0419786 0.5259533 0.4725901 1.411276 0.6877 0.5893 0.4776940 1.433647 0.0579108 22 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 47 500 10 nonlinear nonlinear 0.5815 0.4583964 0.4211952 0.0372012 0.5278874 0.4734171 1.385981 0.6864 0.5574 0.4820067 1.405393 0.0608114 29 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 48 500 10 nonlinear nonlinear 0.5850 0.4991767 0.4193505 0.0798262 0.5242640 0.4723694 1.394973 0.5307 0.5690 0.4827004 1.408507 0.0633499 24 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 49 500 10 nonlinear nonlinear 0.5955 0.4737004 0.4205878 0.0531126 0.5266374 0.4735577 1.393692 0.5755 0.5739 0.4817116 1.443180 0.0611238 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 50 500 10 nonlinear nonlinear 0.5640 0.4410557 0.4209544 0.0201014 0.5277064 0.4740853 1.382522 0.7964 0.5192 0.4946073 1.413855 0.0736530 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 51 500 10 nonlinear nonlinear 0.5930 0.4714187 0.4190060 0.0524127 0.5244915 0.4731688 1.399610 0.6517 0.6632 0.4663782 1.436343 0.0473722 25 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 52 500 10 nonlinear nonlinear 0.5615 0.4729186 0.4200397 0.0528789 0.5252524 0.4735027 1.406870 0.6219 0.5831 0.4808042 1.425411 0.0607646 27 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 53 500 10 nonlinear nonlinear 0.5965 0.4376294 0.4186492 0.0189801 0.5244418 0.4723023 1.384825 0.8091 0.5713 0.4799481 1.426475 0.0612989 27 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 54 500 10 nonlinear nonlinear 0.5420 0.4823990 0.4198013 0.0625976 0.5274167 0.4726808 1.401376 0.5528 0.5495 0.4843074 1.408904 0.0645060 26 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 55 500 10 nonlinear nonlinear 0.6100 0.4727032 0.4210603 0.0516430 0.5248900 0.4754874 1.389729 0.6060 0.5589 0.4873000 1.407269 0.0662397 22 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 56 500 10 nonlinear nonlinear 0.5875 0.4436834 0.4214330 0.0222504 0.5289512 0.4733808 1.375438 0.7750 0.5668 0.4839079 1.415226 0.0624749 27 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 57 500 10 nonlinear nonlinear 0.5685 0.4760803 0.4205036 0.0555767 0.5255058 0.4740665 1.416882 0.5782 0.5875 0.4766647 1.421844 0.0561611 29 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 58 500 10 nonlinear nonlinear 0.5350 0.4693974 0.4208123 0.0485851 0.5264992 0.4740150 1.412750 0.6323 0.5753 0.4843315 1.470523 0.0635192 24 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 59 500 10 nonlinear nonlinear 0.6180 0.4525917 0.4209457 0.0316460 0.5253588 0.4748718 1.384993 0.7238 0.6108 0.4739142 1.406651 0.0529685 22 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 60 500 10 nonlinear nonlinear 0.6060 0.4711581 0.4204099 0.0507483 0.5259651 0.4729849 1.394310 0.5920 0.5349 0.4931107 1.402873 0.0727009 25 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 61 500 10 nonlinear nonlinear 0.5790 0.4798054 0.4199333 0.0598721 0.5257447 0.4726339 1.397189 0.5645 0.5666 0.4803619 1.409272 0.0604286 25 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 62 500 10 nonlinear nonlinear 0.5060 0.4489912 0.4197676 0.0292236 0.5251160 0.4737161 1.394908 0.7459 0.5413 0.4822103 1.423313 0.0624426 25 0.4 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 63 500 10 nonlinear nonlinear 0.6190 0.4523216 0.4210589 0.0312628 0.5264116 0.4753762 1.386053 0.7377 0.5870 0.4809864 1.404916 0.0599275 27 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 64 500 10 nonlinear nonlinear 0.5120 0.4657526 0.4201733 0.0455793 0.5271510 0.4729743 1.400968 0.6378 0.5625 0.4804603 1.430802 0.0602870 25 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 65 500 10 nonlinear nonlinear 0.5480 0.4871838 0.4195956 0.0675883 0.5241065 0.4729791 1.403922 0.5671 0.5669 0.4811472 1.451020 0.0615516 23 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 66 500 10 nonlinear nonlinear 0.5965 0.4961642 0.4207898 0.0753744 0.5271670 0.4739597 1.383392 0.5386 0.5658 0.4838123 1.410495 0.0630225 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 67 500 10 nonlinear nonlinear 0.5415 0.4510225 0.4215395 0.0294830 0.5255740 0.4760960 1.394117 0.7511 0.5345 0.4906461 1.438840 0.0691066 26 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 68 500 10 nonlinear nonlinear 0.5315 0.4761547 0.4213815 0.0547733 0.5272388 0.4745261 1.413613 0.6153 0.5499 0.4903715 1.431748 0.0689900 26 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 69 500 10 nonlinear nonlinear 0.5790 0.4607852 0.4209668 0.0398184 0.5261109 0.4748459 1.397655 0.6908 0.6161 0.4757332 1.433470 0.0547664 30 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 70 500 10 nonlinear nonlinear 0.5160 0.4827535 0.4190538 0.0636997 0.5252249 0.4722170 1.448622 0.5421 0.4968 0.4960424 1.432039 0.0769886 30 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 71 500 10 nonlinear nonlinear 0.5680 0.4778419 0.4203780 0.0574639 0.5276008 0.4726068 1.405533 0.6096 0.5746 0.4783907 1.435290 0.0580128 24 0.6 0.8 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 72 500 10 nonlinear nonlinear 0.5310 0.4593243 0.4199248 0.0393994 0.5251372 0.4740735 1.411887 0.6765 0.5654 0.4817456 1.431707 0.0618207 30 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 73 500 10 nonlinear nonlinear 0.5695 0.4722702 0.4212427 0.0510275 0.5279249 0.4744110 1.397474 0.6018 0.5635 0.4837112 1.425752 0.0624686 29 0.1 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 74 500 10 nonlinear nonlinear 0.5470 0.4714879 0.4197057 0.0517822 0.5262347 0.4719815 1.388747 0.6310 0.5748 0.4785453 1.404456 0.0588396 24 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 75 500 10 nonlinear nonlinear 0.5685 0.4987953 0.4213275 0.0774678 0.5275726 0.4740060 1.421860 0.5011 0.5341 0.4900156 1.421500 0.0686881 27 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 76 500 10 nonlinear nonlinear 0.5325 0.5063717 0.4212118 0.0851599 0.5264289 0.4762162 1.427782 0.5099 0.4954 0.5030431 1.424836 0.0818313 28 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 77 500 10 nonlinear nonlinear 0.5525 0.4578703 0.4205448 0.0373254 0.5269289 0.4731279 1.398104 0.7053 0.5820 0.4816224 1.436982 0.0610775 32 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 78 500 10 nonlinear nonlinear 0.5680 0.4751420 0.4218253 0.0533167 0.5279027 0.4754195 1.390004 0.6275 0.5865 0.4798205 1.398267 0.0579952 25 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 79 500 10 nonlinear nonlinear 0.6015 0.4967085 0.4192647 0.0774439 0.5256564 0.4725410 1.404082 0.5360 0.5954 0.4780236 1.420171 0.0587589 24 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 80 500 10 nonlinear nonlinear 0.5575 0.4804494 0.4202843 0.0601651 0.5268323 0.4733200 1.408400 0.5640 0.5818 0.4782287 1.421077 0.0579444 22 0.5 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 81 500 10 nonlinear nonlinear 0.5840 0.4645025 0.4217524 0.0427501 0.5263726 0.4756749 1.380953 0.6697 0.5547 0.4908564 1.415397 0.0691040 23 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 82 500 10 nonlinear nonlinear 0.5545 0.4389487 0.4208063 0.0181424 0.5260003 0.4741356 1.375906 0.8097 0.5544 0.4886543 1.429227 0.0678481 26 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 83 500 10 nonlinear nonlinear 0.5535 0.4904211 0.4209958 0.0694253 0.5264896 0.4743762 1.419178 0.5282 0.5532 0.4874122 1.417706 0.0664164 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 84 500 10 nonlinear nonlinear 0.6080 0.4803293 0.4197605 0.0605688 0.5264119 0.4725703 1.401795 0.5670 0.5603 0.4829712 1.411757 0.0632107 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 85 500 10 nonlinear nonlinear 0.5250 0.4612097 0.4202126 0.0409971 0.5265759 0.4732453 1.404000 0.6701 0.5751 0.4776672 1.440275 0.0574546 28 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 86 500 10 nonlinear nonlinear 0.5545 0.4953245 0.4195045 0.0758200 0.5254324 0.4735797 1.400616 0.5611 0.6276 0.4741938 1.431182 0.0546894 24 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 87 500 10 nonlinear nonlinear 0.5760 0.5039229 0.4201693 0.0837536 0.5246845 0.4738913 1.409181 0.5151 0.5831 0.4850536 1.416018 0.0648843 25 0.6 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 88 500 10 nonlinear nonlinear 0.5955 0.4560598 0.4205879 0.0354720 0.5266736 0.4737973 1.376702 0.7001 0.6560 0.4674602 1.403951 0.0468724 27 0.3 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 89 500 10 nonlinear nonlinear 0.5675 0.4637186 0.4210246 0.0426940 0.5263781 0.4742359 1.382598 0.6872 0.5774 0.4793195 1.412654 0.0582949 20 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 90 500 10 nonlinear nonlinear 0.5845 0.5006620 0.4196209 0.0810412 0.5257459 0.4724138 1.401383 0.5021 0.5352 0.4912003 1.426955 0.0715795 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 91 500 10 nonlinear nonlinear 0.5485 0.4561291 0.4219036 0.0342256 0.5270563 0.4746389 1.384111 0.7057 0.5528 0.4853196 1.454065 0.0634160 29 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 92 500 10 nonlinear nonlinear 0.5640 0.4777117 0.4205228 0.0571888 0.5276902 0.4736251 1.409774 0.5819 0.5878 0.4793053 1.439757 0.0587824 28 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 93 500 10 nonlinear nonlinear 0.5865 0.4653970 0.4183454 0.0470516 0.5244125 0.4713449 1.407638 0.6650 0.6504 0.4671669 1.462347 0.0488215 27 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 94 500 10 nonlinear nonlinear 0.5000 0.4500989 0.4204986 0.0296003 0.5257978 0.4738906 1.406245 0.7503 0.5754 0.4829633 1.423128 0.0624647 24 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 95 500 10 nonlinear nonlinear 0.4895 0.4854031 0.4206257 0.0647774 0.5278232 0.4730967 1.415205 0.5752 0.5664 0.4849914 1.456682 0.0643657 22 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 96 500 10 nonlinear nonlinear 0.6390 0.4706118 0.4209503 0.0496615 0.5275576 0.4738755 1.383323 0.6357 0.4703 0.5051549 1.421517 0.0842047 29 0.4 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 97 500 10 nonlinear nonlinear 0.5080 0.4791728 0.4207969 0.0583760 0.5243718 0.4743133 1.400263 0.5805 0.5529 0.4889790 1.436821 0.0681821 24 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 98 500 10 nonlinear nonlinear 0.4955 0.4684783 0.4225979 0.0458804 0.5262883 0.4775202 1.414378 0.6719 0.5544 0.4863125 1.434003 0.0637147 30 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 99 500 10 nonlinear nonlinear 0.4675 0.4808238 0.4200244 0.0607993 0.5268581 0.4731183 1.428551 0.5730 0.5536 0.4871281 1.428641 0.0671036 27 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
22 100 500 10 nonlinear nonlinear 0.5905 0.4425862 0.4207695 0.0218167 0.5285793 0.4737461 1.379112 0.7841 0.5685 0.4808442 1.413446 0.0600747 24 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
23 1 1000 10 nonlinear nonlinear 0.6020 0.4441704 0.4203927 0.0237777 0.5266984 0.4737390 1.386618 0.7800 0.5910 0.4787776 1.416248 0.0583849 38 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 2 1000 10 nonlinear nonlinear 0.6000 0.4376773 0.4205496 0.0171277 0.5263821 0.4737082 1.368029 0.8161 0.6165 0.4757393 1.401795 0.0551897 28 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 3 1000 10 nonlinear nonlinear 0.5650 0.4642357 0.4207905 0.0434452 0.5284262 0.4735158 1.381916 0.6922 0.5955 0.4801630 1.399999 0.0593726 29 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 4 1000 10 nonlinear nonlinear 0.6415 0.4423312 0.4212913 0.0210399 0.5270494 0.4749983 1.366189 0.7890 0.5764 0.4814747 1.395931 0.0601833 29 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 5 1000 10 nonlinear nonlinear 0.5840 0.4337704 0.4206580 0.0131125 0.5257056 0.4733316 1.373207 0.8387 0.5695 0.4768770 1.404440 0.0562190 31 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 6 1000 10 nonlinear nonlinear 0.6265 0.4462695 0.4214326 0.0248369 0.5273825 0.4740242 1.358777 0.7616 0.5748 0.4791276 1.381978 0.0576950 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 7 1000 10 nonlinear nonlinear 0.6170 0.4793420 0.4187954 0.0605466 0.5260599 0.4718553 1.388821 0.5744 0.5838 0.4783423 1.396714 0.0595469 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 8 1000 10 nonlinear nonlinear 0.6390 0.4426161 0.4211527 0.0214635 0.5257856 0.4746086 1.371496 0.7892 0.6105 0.4748003 1.392458 0.0536476 34 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 9 1000 10 nonlinear nonlinear 0.5945 0.4640441 0.4208991 0.0431451 0.5254118 0.4750982 1.381078 0.6378 0.5584 0.4773436 1.399668 0.0564445 30 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 10 1000 10 nonlinear nonlinear 0.6205 0.4580829 0.4203623 0.0377206 0.5276013 0.4721085 1.381559 0.7102 0.6527 0.4688662 1.415561 0.0485039 31 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 11 1000 10 nonlinear nonlinear 0.5650 0.4604231 0.4202899 0.0401332 0.5261327 0.4733620 1.387673 0.6961 0.6387 0.4700134 1.406656 0.0497235 34 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 12 1000 10 nonlinear nonlinear 0.5910 0.4438071 0.4199206 0.0238864 0.5258739 0.4736092 1.371443 0.7845 0.5723 0.4790972 1.395939 0.0591766 33 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 13 1000 10 nonlinear nonlinear 0.6465 0.4433425 0.4209056 0.0224370 0.5264114 0.4737496 1.361797 0.7899 0.5785 0.4802340 1.391730 0.0593284 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 14 1000 10 nonlinear nonlinear 0.5760 0.4428248 0.4215985 0.0212263 0.5271383 0.4744528 1.370838 0.7868 0.5667 0.4802019 1.398322 0.0586034 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 15 1000 10 nonlinear nonlinear 0.5950 0.4509599 0.4207302 0.0302297 0.5266392 0.4737143 1.379057 0.7371 0.5660 0.4774903 1.409428 0.0567601 30 0.3 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 16 1000 10 nonlinear nonlinear 0.5810 0.4351490 0.4212283 0.0139208 0.5270841 0.4741347 1.365997 0.8448 0.6337 0.4713891 1.391963 0.0501608 35 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 17 1000 10 nonlinear nonlinear 0.5870 0.4465587 0.4210428 0.0255158 0.5277253 0.4744456 1.365965 0.7696 0.5848 0.4788415 1.387895 0.0577986 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 18 1000 10 nonlinear nonlinear 0.6215 0.4879304 0.4195276 0.0684028 0.5262034 0.4724210 1.402299 0.5630 0.6270 0.4730471 1.401076 0.0535195 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 19 1000 10 nonlinear nonlinear 0.5850 0.4433943 0.4221372 0.0212571 0.5279211 0.4757238 1.370669 0.7879 0.5774 0.4809546 1.389302 0.0588174 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 20 1000 10 nonlinear nonlinear 0.5960 0.4519778 0.4209477 0.0310300 0.5262166 0.4738648 1.377541 0.7257 0.5797 0.4804809 1.393836 0.0595331 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 21 1000 10 nonlinear nonlinear 0.6250 0.4583997 0.4221448 0.0362550 0.5284795 0.4751328 1.372797 0.7156 0.5795 0.4817813 1.393293 0.0596365 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 22 1000 10 nonlinear nonlinear 0.6060 0.4423652 0.4209154 0.0214499 0.5270862 0.4746316 1.373902 0.7871 0.5739 0.4790631 1.400163 0.0581477 29 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 23 1000 10 nonlinear nonlinear 0.5715 0.4447453 0.4198957 0.0248496 0.5260044 0.4729394 1.375256 0.7656 0.5600 0.4786861 1.387368 0.0587904 40 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 24 1000 10 nonlinear nonlinear 0.6250 0.4593391 0.4202617 0.0390774 0.5272764 0.4721642 1.385078 0.6846 0.5766 0.4770362 1.406489 0.0567745 28 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 25 1000 10 nonlinear nonlinear 0.6235 0.4645591 0.4210078 0.0435513 0.5251455 0.4753799 1.377829 0.6669 0.5547 0.4856144 1.405293 0.0646066 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 26 1000 10 nonlinear nonlinear 0.5810 0.4307061 0.4187362 0.0119699 0.5265275 0.4709281 1.360475 0.8495 0.5749 0.4774686 1.380901 0.0587324 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 27 1000 10 nonlinear nonlinear 0.5945 0.4517908 0.4201205 0.0316702 0.5249348 0.4742531 1.371142 0.7163 0.5753 0.4807258 1.401785 0.0606052 35 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 28 1000 10 nonlinear nonlinear 0.5455 0.4471415 0.4207982 0.0263434 0.5275529 0.4739125 1.384270 0.7675 0.5994 0.4785012 1.401650 0.0577031 29 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 29 1000 10 nonlinear nonlinear 0.5930 0.4408595 0.4222238 0.0186357 0.5282481 0.4748775 1.372441 0.8037 0.5885 0.4788275 1.396356 0.0566037 29 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 30 1000 10 nonlinear nonlinear 0.6075 0.4641348 0.4205793 0.0435555 0.5261532 0.4741585 1.371213 0.6739 0.5590 0.4840711 1.402351 0.0634918 31 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 31 1000 10 nonlinear nonlinear 0.6245 0.4451281 0.4206952 0.0244329 0.5250245 0.4741049 1.367395 0.7613 0.5692 0.4793969 1.391027 0.0587016 27 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 32 1000 10 nonlinear nonlinear 0.6455 0.4516494 0.4210407 0.0306087 0.5265728 0.4741269 1.380739 0.7244 0.5507 0.4817142 1.391449 0.0606735 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 33 1000 10 nonlinear nonlinear 0.6125 0.4491467 0.4204988 0.0286479 0.5260892 0.4732896 1.371946 0.7327 0.5804 0.4780963 1.397943 0.0575975 30 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 34 1000 10 nonlinear nonlinear 0.6115 0.4358810 0.4206252 0.0152559 0.5275948 0.4726848 1.360544 0.8230 0.5770 0.4790699 1.389895 0.0584448 31 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 35 1000 10 nonlinear nonlinear 0.5640 0.4390980 0.4194965 0.0196015 0.5248901 0.4739398 1.365704 0.8068 0.5656 0.4791854 1.388227 0.0596889 31 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 36 1000 10 nonlinear nonlinear 0.5715 0.4540796 0.4221647 0.0319149 0.5275017 0.4742793 1.376290 0.7248 0.5610 0.4818625 1.391416 0.0596978 31 0.7 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 37 1000 10 nonlinear nonlinear 0.5975 0.4399654 0.4227295 0.0172359 0.5267839 0.4759743 1.368427 0.8148 0.5792 0.4828993 1.402175 0.0601698 34 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 38 1000 10 nonlinear nonlinear 0.6010 0.4311183 0.4194318 0.0116866 0.5243022 0.4729886 1.370768 0.8515 0.5664 0.4782838 1.392548 0.0588521 31 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 39 1000 10 nonlinear nonlinear 0.6390 0.4775728 0.4206596 0.0569132 0.5252333 0.4747893 1.392370 0.5869 0.5933 0.4775032 1.405030 0.0568437 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 40 1000 10 nonlinear nonlinear 0.5395 0.4437216 0.4208864 0.0228352 0.5267458 0.4741450 1.372075 0.7804 0.5362 0.4915478 1.417021 0.0706614 32 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 41 1000 10 nonlinear nonlinear 0.5985 0.4382667 0.4218523 0.0164144 0.5284555 0.4750977 1.382947 0.8344 0.6842 0.4646408 1.405161 0.0427885 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 42 1000 10 nonlinear nonlinear 0.5940 0.4364571 0.4201203 0.0163367 0.5280101 0.4728784 1.375811 0.8257 0.6360 0.4698070 1.400208 0.0496866 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 43 1000 10 nonlinear nonlinear 0.6005 0.4435867 0.4218859 0.0217008 0.5268513 0.4745339 1.362156 0.7871 0.6145 0.4745855 1.381358 0.0526996 32 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 44 1000 10 nonlinear nonlinear 0.6035 0.4526767 0.4210066 0.0316701 0.5257971 0.4751728 1.376100 0.7234 0.5772 0.4792980 1.394565 0.0582914 37 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 45 1000 10 nonlinear nonlinear 0.5510 0.4420250 0.4227191 0.0193059 0.5280745 0.4758741 1.379799 0.8073 0.5808 0.4822674 1.400048 0.0595483 31 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 46 1000 10 nonlinear nonlinear 0.6220 0.4408693 0.4201005 0.0207688 0.5247056 0.4739935 1.371179 0.7940 0.5763 0.4785829 1.392737 0.0584823 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 47 1000 10 nonlinear nonlinear 0.6355 0.4365385 0.4214536 0.0150849 0.5267735 0.4744455 1.368270 0.8251 0.5709 0.4786431 1.395753 0.0571896 34 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 48 1000 10 nonlinear nonlinear 0.5915 0.4497473 0.4207440 0.0290033 0.5260676 0.4736604 1.377016 0.7529 0.5530 0.4853656 1.403393 0.0646215 31 0.6 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 49 1000 10 nonlinear nonlinear 0.5975 0.4948821 0.4206704 0.0742117 0.5248508 0.4745340 1.391457 0.4966 0.5769 0.4803797 1.389299 0.0597093 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 50 1000 10 nonlinear nonlinear 0.5835 0.4568184 0.4192097 0.0376087 0.5266034 0.4725070 1.379129 0.6881 0.5754 0.4766660 1.386681 0.0574564 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 51 1000 10 nonlinear nonlinear 0.5975 0.4377461 0.4208210 0.0169251 0.5266688 0.4741064 1.376365 0.8210 0.6505 0.4682597 1.396946 0.0474387 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 52 1000 10 nonlinear nonlinear 0.6030 0.4398431 0.4211589 0.0186842 0.5259466 0.4747331 1.374097 0.8055 0.5647 0.4801554 1.386910 0.0589965 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 53 1000 10 nonlinear nonlinear 0.6355 0.4368496 0.4194739 0.0173757 0.5244447 0.4730613 1.364541 0.8163 0.5727 0.4764913 1.387652 0.0570174 36 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 54 1000 10 nonlinear nonlinear 0.6215 0.4897595 0.4226636 0.0670960 0.5279833 0.4758828 1.400443 0.5632 0.5609 0.4792666 1.399456 0.0566031 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 55 1000 10 nonlinear nonlinear 0.6475 0.4597166 0.4207389 0.0389777 0.5276640 0.4738543 1.378553 0.7061 0.5863 0.4780388 1.396946 0.0572999 33 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 56 1000 10 nonlinear nonlinear 0.5945 0.4824191 0.4206863 0.0617328 0.5263593 0.4745498 1.398270 0.5701 0.5653 0.4797748 1.397723 0.0590885 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 57 1000 10 nonlinear nonlinear 0.5970 0.4361146 0.4207387 0.0153759 0.5273156 0.4732082 1.372960 0.8267 0.5752 0.4806150 1.402831 0.0598763 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 58 1000 10 nonlinear nonlinear 0.5845 0.4422382 0.4209393 0.0212988 0.5258950 0.4740056 1.375010 0.7882 0.5785 0.4778981 1.395938 0.0569588 35 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 59 1000 10 nonlinear nonlinear 0.6255 0.4530042 0.4210459 0.0319584 0.5253819 0.4760595 1.377720 0.7547 0.5650 0.4813075 1.404206 0.0602617 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 60 1000 10 nonlinear nonlinear 0.6235 0.4402407 0.4215633 0.0186774 0.5264838 0.4741040 1.367932 0.8023 0.6135 0.4743908 1.404889 0.0528274 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 61 1000 10 nonlinear nonlinear 0.5930 0.4731471 0.4220356 0.0511115 0.5262455 0.4753485 1.383230 0.6042 0.5107 0.4948324 1.387967 0.0727968 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 62 1000 10 nonlinear nonlinear 0.6395 0.4348909 0.4204965 0.0143944 0.5279952 0.4738075 1.364235 0.8471 0.5288 0.4856534 1.386008 0.0651569 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 63 1000 10 nonlinear nonlinear 0.5970 0.4480847 0.4193610 0.0287237 0.5247325 0.4730071 1.361441 0.7390 0.5761 0.4816440 1.397369 0.0622830 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 64 1000 10 nonlinear nonlinear 0.5810 0.4492236 0.4206721 0.0285515 0.5263781 0.4735253 1.378056 0.7551 0.6001 0.4752367 1.390540 0.0545646 29 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 65 1000 10 nonlinear nonlinear 0.6185 0.4640641 0.4193865 0.0446776 0.5280926 0.4720669 1.373277 0.6768 0.6179 0.4728566 1.392963 0.0534701 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 66 1000 10 nonlinear nonlinear 0.5870 0.4292158 0.4200480 0.0091678 0.5258507 0.4734669 1.356245 0.8698 0.5774 0.4780082 1.380411 0.0579602 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 67 1000 10 nonlinear nonlinear 0.5760 0.4337381 0.4202739 0.0134642 0.5263010 0.4734742 1.366324 0.8378 0.5879 0.4768437 1.398169 0.0565698 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 68 1000 10 nonlinear nonlinear 0.6370 0.4387642 0.4178757 0.0208885 0.5247461 0.4703784 1.370968 0.7867 0.5727 0.4760665 1.400931 0.0581907 27 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 69 1000 10 nonlinear nonlinear 0.6040 0.4355123 0.4189580 0.0165543 0.5245540 0.4731233 1.364714 0.8210 0.5762 0.4767730 1.381818 0.0578150 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 70 1000 10 nonlinear nonlinear 0.6260 0.4262225 0.4196144 0.0066081 0.5246048 0.4734752 1.364476 0.8927 0.5657 0.4770105 1.392369 0.0573961 30 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 71 1000 10 nonlinear nonlinear 0.6460 0.4473131 0.4193509 0.0279622 0.5264774 0.4710452 1.374182 0.7438 0.5739 0.4783691 1.400961 0.0590182 29 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 72 1000 10 nonlinear nonlinear 0.6080 0.4334358 0.4206841 0.0127517 0.5272147 0.4741167 1.358778 0.8448 0.5756 0.4789198 1.392091 0.0582357 30 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 73 1000 10 nonlinear nonlinear 0.6175 0.4414807 0.4207333 0.0207474 0.5271970 0.4743948 1.377452 0.7984 0.5501 0.4900370 1.401380 0.0693036 20 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 74 1000 10 nonlinear nonlinear 0.6110 0.4377720 0.4215993 0.0161728 0.5276036 0.4752943 1.368494 0.8477 0.5968 0.4794005 1.393950 0.0578012 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 75 1000 10 nonlinear nonlinear 0.6430 0.4455934 0.4192474 0.0263460 0.5246658 0.4729196 1.362540 0.7485 0.6238 0.4704124 1.376888 0.0511650 32 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 76 1000 10 nonlinear nonlinear 0.5965 0.4626062 0.4199196 0.0426866 0.5247992 0.4733497 1.389004 0.6889 0.5834 0.4770513 1.403170 0.0571318 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 77 1000 10 nonlinear nonlinear 0.6045 0.4648591 0.4196210 0.0452380 0.5277984 0.4716956 1.375544 0.6416 0.6004 0.4733504 1.387217 0.0537294 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 78 1000 10 nonlinear nonlinear 0.5845 0.4251341 0.4202237 0.0049104 0.5263087 0.4731257 1.363924 0.9112 0.6090 0.4763536 1.394975 0.0561298 31 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 79 1000 10 nonlinear nonlinear 0.5335 0.4663765 0.4202310 0.0461455 0.5257961 0.4736503 1.379713 0.6479 0.5581 0.4810986 1.394874 0.0608676 33 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 80 1000 10 nonlinear nonlinear 0.6165 0.4379044 0.4209375 0.0169669 0.5275730 0.4737852 1.367271 0.8219 0.5881 0.4773788 1.390068 0.0564414 28 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 81 1000 10 nonlinear nonlinear 0.6145 0.4496284 0.4208158 0.0288126 0.5261279 0.4743117 1.379868 0.7525 0.5785 0.4825523 1.408824 0.0617364 30 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 82 1000 10 nonlinear nonlinear 0.4900 0.4746294 0.4210795 0.0535499 0.5257271 0.4743923 1.409229 0.5971 0.5840 0.4786710 1.383713 0.0575915 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 83 1000 10 nonlinear nonlinear 0.5935 0.4379492 0.4222370 0.0157122 0.5276041 0.4767839 1.371840 0.8314 0.5960 0.4816134 1.386895 0.0593763 26 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 84 1000 10 nonlinear nonlinear 0.5850 0.4515188 0.4205527 0.0309661 0.5266425 0.4733521 1.394339 0.7361 0.5722 0.4819534 1.399312 0.0614007 31 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 85 1000 10 nonlinear nonlinear 0.6310 0.4490750 0.4208594 0.0282155 0.5275820 0.4739215 1.370919 0.7604 0.6624 0.4669156 1.392165 0.0460562 25 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 86 1000 10 nonlinear nonlinear 0.5635 0.4476728 0.4215779 0.0260949 0.5272758 0.4747566 1.367225 0.7657 0.6054 0.4763314 1.386070 0.0547535 34 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 87 1000 10 nonlinear nonlinear 0.6110 0.4575643 0.4200573 0.0375070 0.5257330 0.4732171 1.378443 0.7033 0.6614 0.4666769 1.388198 0.0466196 30 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 88 1000 10 nonlinear nonlinear 0.5445 0.4576563 0.4215448 0.0361115 0.5260062 0.4756224 1.379144 0.7148 0.5616 0.4852286 1.406597 0.0636838 34 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 89 1000 10 nonlinear nonlinear 0.5650 0.4487291 0.4207691 0.0279600 0.5257687 0.4746506 1.374481 0.7528 0.5953 0.4764014 1.389649 0.0556323 35 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 90 1000 10 nonlinear nonlinear 0.5945 0.4997902 0.4215471 0.0782431 0.5269427 0.4748476 1.382263 0.5254 0.5525 0.4895454 1.392129 0.0679983 29 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 91 1000 10 nonlinear nonlinear 0.6130 0.4508542 0.4190655 0.0317887 0.5257084 0.4715649 1.372499 0.7183 0.6001 0.4745168 1.394365 0.0554513 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 92 1000 10 nonlinear nonlinear 0.6085 0.4288006 0.4186912 0.0101094 0.5262168 0.4709942 1.360902 0.8642 0.5814 0.4757780 1.393616 0.0570868 29 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 93 1000 10 nonlinear nonlinear 0.6140 0.4399603 0.4199071 0.0200531 0.5256457 0.4726083 1.378286 0.7954 0.5885 0.4776034 1.404479 0.0576963 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 94 1000 10 nonlinear nonlinear 0.6045 0.4373547 0.4219895 0.0153652 0.5256370 0.4766871 1.372368 0.8280 0.5505 0.4859781 1.412981 0.0639886 26 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 95 1000 10 nonlinear nonlinear 0.5460 0.4645596 0.4218781 0.0426816 0.5279352 0.4748574 1.378058 0.6718 0.7008 0.4618858 1.391621 0.0400077 23 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 96 1000 10 nonlinear nonlinear 0.5945 0.4564173 0.4204331 0.0359842 0.5262443 0.4742864 1.378966 0.6991 0.5679 0.4798075 1.409504 0.0593743 29 0.6 0.9 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 97 1000 10 nonlinear nonlinear 0.5285 0.4437479 0.4203313 0.0234166 0.5261144 0.4738623 1.376473 0.7837 0.5716 0.4791366 1.386350 0.0588053 29 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 98 1000 10 nonlinear nonlinear 0.6270 0.4643511 0.4213414 0.0430097 0.5268501 0.4747153 1.382251 0.6474 0.5788 0.4785692 1.391394 0.0572277 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 99 1000 10 nonlinear nonlinear 0.5770 0.4381492 0.4202602 0.0178890 0.5246876 0.4738115 1.375622 0.8106 0.5349 0.4855754 1.397269 0.0653151 28 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
23 100 1000 10 nonlinear nonlinear 0.6340 0.4486590 0.4196778 0.0289811 0.5266643 0.4722434 1.370996 0.7381 0.5596 0.4801611 1.398978 0.0604832 30 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 1 2000 10 nonlinear nonlinear 0.5975 0.4298230 0.4202673 0.0095557 0.5261267 0.4743478 1.361918 0.8699 0.5820 0.4781453 1.387444 0.0578780 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 2 2000 10 nonlinear nonlinear 0.5740 0.4277920 0.4208823 0.0069097 0.5273556 0.4737392 1.363417 0.8906 0.6031 0.4758139 1.388543 0.0549317 40 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 3 2000 10 nonlinear nonlinear 0.5855 0.4425787 0.4205554 0.0220233 0.5279946 0.4727395 1.366077 0.8198 0.5810 0.4776643 1.381921 0.0571089 43 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 4 2000 10 nonlinear nonlinear 0.6020 0.4322465 0.4211936 0.0110529 0.5271740 0.4746087 1.363307 0.8621 0.6228 0.4728074 1.382782 0.0516139 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 5 2000 10 nonlinear nonlinear 0.6225 0.4304011 0.4197672 0.0106339 0.5240696 0.4745718 1.354672 0.8596 0.5728 0.4776384 1.377613 0.0578712 41 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 6 2000 10 nonlinear nonlinear 0.6485 0.4423661 0.4227156 0.0196505 0.5296427 0.4747591 1.363922 0.7998 0.6058 0.4771453 1.380619 0.0544297 35 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 7 2000 10 nonlinear nonlinear 0.6380 0.4377926 0.4210810 0.0167116 0.5262444 0.4736577 1.364270 0.8192 0.5559 0.4809545 1.382556 0.0598735 42 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 8 2000 10 nonlinear nonlinear 0.6140 0.4299720 0.4225371 0.0074349 0.5267474 0.4760432 1.361244 0.8853 0.5901 0.4803098 1.383072 0.0577727 35 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 9 2000 10 nonlinear nonlinear 0.5820 0.4307121 0.4208363 0.0098758 0.5269813 0.4740924 1.359431 0.8636 0.5814 0.4787716 1.391457 0.0579353 39 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 10 2000 10 nonlinear nonlinear 0.5875 0.4353468 0.4218131 0.0135337 0.5275819 0.4750718 1.367675 0.8379 0.6217 0.4786644 1.388562 0.0568514 47 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 11 2000 10 nonlinear nonlinear 0.5250 0.4486237 0.4207031 0.0279207 0.5260482 0.4743681 1.366380 0.7444 0.5423 0.4823067 1.386834 0.0616036 38 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 12 2000 10 nonlinear nonlinear 0.6465 0.4323260 0.4177336 0.0145924 0.5261379 0.4698930 1.359607 0.8369 0.6260 0.4694195 1.378748 0.0516860 39 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 13 2000 10 nonlinear nonlinear 0.6515 0.4318638 0.4211329 0.0107309 0.5268550 0.4742147 1.361701 0.8591 0.5918 0.4768482 1.386575 0.0557153 47 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 14 2000 10 nonlinear nonlinear 0.6320 0.4299761 0.4190609 0.0109152 0.5271250 0.4719475 1.353297 0.8584 0.5478 0.4820375 1.376676 0.0629766 44 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 15 2000 10 nonlinear nonlinear 0.6085 0.4298247 0.4198672 0.0099574 0.5236663 0.4738512 1.362936 0.8611 0.5611 0.4802348 1.387934 0.0603676 35 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 16 2000 10 nonlinear nonlinear 0.6205 0.4341656 0.4177155 0.0164501 0.5248767 0.4708651 1.362897 0.8267 0.5799 0.4755936 1.381242 0.0578781 41 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 17 2000 10 nonlinear nonlinear 0.6095 0.4427185 0.4204248 0.0222937 0.5258568 0.4737754 1.367596 0.7824 0.5822 0.4773886 1.378031 0.0569637 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 18 2000 10 nonlinear nonlinear 0.6100 0.4225911 0.4194669 0.0031243 0.5254676 0.4732760 1.363499 0.9291 0.5642 0.4803649 1.389565 0.0608980 37 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 19 2000 10 nonlinear nonlinear 0.6445 0.4366608 0.4204198 0.0162410 0.5256466 0.4742067 1.366161 0.8239 0.6058 0.4747122 1.388070 0.0542924 38 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 20 2000 10 nonlinear nonlinear 0.6215 0.4405119 0.4215511 0.0189608 0.5292756 0.4731675 1.363948 0.8073 0.5898 0.4783590 1.382429 0.0568079 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 21 2000 10 nonlinear nonlinear 0.5955 0.4331178 0.4196557 0.0134621 0.5239352 0.4735622 1.362670 0.8397 0.6080 0.4728395 1.383825 0.0531838 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 22 2000 10 nonlinear nonlinear 0.5705 0.4382059 0.4204153 0.0177906 0.5262100 0.4744371 1.363335 0.8104 0.5879 0.4768550 1.390260 0.0564397 43 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 23 2000 10 nonlinear nonlinear 0.5500 0.4297846 0.4211530 0.0086315 0.5260053 0.4751317 1.359787 0.8731 0.5746 0.4789441 1.383816 0.0577911 37 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 24 2000 10 nonlinear nonlinear 0.5770 0.4358224 0.4217919 0.0140305 0.5258849 0.4756179 1.376121 0.8363 0.5755 0.4815032 1.389138 0.0597113 36 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 25 2000 10 nonlinear nonlinear 0.6115 0.4292358 0.4220144 0.0072214 0.5277663 0.4750550 1.357220 0.8884 0.5878 0.4793130 1.382909 0.0572986 38 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 26 2000 10 nonlinear nonlinear 0.6540 0.4323835 0.4188732 0.0135103 0.5256283 0.4720891 1.361788 0.8412 0.5710 0.4774981 1.379984 0.0586248 40 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 27 2000 10 nonlinear nonlinear 0.6010 0.4255794 0.4218309 0.0037484 0.5270934 0.4760121 1.353532 0.9213 0.6037 0.4767556 1.385625 0.0549247 39 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 28 2000 10 nonlinear nonlinear 0.6270 0.4495637 0.4201055 0.0294582 0.5257661 0.4731437 1.368039 0.7612 0.5735 0.4781297 1.391351 0.0580241 37 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 29 2000 10 nonlinear nonlinear 0.6015 0.4376760 0.4221015 0.0155744 0.5300631 0.4741490 1.366188 0.8253 0.5881 0.4789378 1.395061 0.0568363 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 30 2000 10 nonlinear nonlinear 0.5945 0.4343157 0.4189184 0.0153973 0.5245660 0.4724582 1.359733 0.8299 0.5351 0.4828943 1.383169 0.0639759 34 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 31 2000 10 nonlinear nonlinear 0.6065 0.4407572 0.4203872 0.0203700 0.5269610 0.4723456 1.367413 0.7916 0.5650 0.4798902 1.382459 0.0595030 41 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 32 2000 10 nonlinear nonlinear 0.5810 0.4515882 0.4209270 0.0306612 0.5281013 0.4725308 1.367251 0.7416 0.6373 0.4701438 1.379943 0.0492168 40 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 33 2000 10 nonlinear nonlinear 0.6020 0.4241568 0.4194221 0.0047347 0.5262241 0.4718798 1.367386 0.9104 0.5562 0.4803680 1.389147 0.0609459 41 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 34 2000 10 nonlinear nonlinear 0.6455 0.4402104 0.4208061 0.0194043 0.5257955 0.4747314 1.364567 0.7974 0.5405 0.4844778 1.385010 0.0636717 36 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 35 2000 10 nonlinear nonlinear 0.5960 0.4515132 0.4199416 0.0315717 0.5263746 0.4725679 1.361139 0.7819 0.5734 0.4777627 1.378441 0.0578211 35 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 36 2000 10 nonlinear nonlinear 0.6015 0.4424445 0.4199404 0.0225042 0.5260302 0.4731158 1.369820 0.7828 0.6074 0.4737413 1.386445 0.0538009 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 37 2000 10 nonlinear nonlinear 0.6225 0.4287453 0.4213323 0.0074131 0.5278808 0.4737528 1.355272 0.8824 0.5892 0.4769556 1.382702 0.0556233 45 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 38 2000 10 nonlinear nonlinear 0.5985 0.4323797 0.4198673 0.0125123 0.5275948 0.4720922 1.359919 0.8302 0.5938 0.4781957 1.385914 0.0583283 38 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 39 2000 10 nonlinear nonlinear 0.6360 0.4366598 0.4220650 0.0145948 0.5280032 0.4745783 1.357408 0.8490 0.6664 0.4660748 1.382622 0.0440098 42 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 40 2000 10 nonlinear nonlinear 0.5855 0.4495440 0.4211694 0.0283746 0.5289865 0.4732757 1.372080 0.7534 0.6082 0.4770634 1.384674 0.0558940 47 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 41 2000 10 nonlinear nonlinear 0.6265 0.4288284 0.4205877 0.0082406 0.5260169 0.4744426 1.354216 0.8686 0.5839 0.4783156 1.381801 0.0577279 40 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 42 2000 10 nonlinear nonlinear 0.6020 0.4392326 0.4204880 0.0187446 0.5263984 0.4736612 1.361202 0.8051 0.5899 0.4755441 1.380245 0.0550561 42 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 43 2000 10 nonlinear nonlinear 0.6210 0.4265118 0.4210257 0.0054860 0.5255202 0.4761809 1.352356 0.9015 0.6450 0.4699144 1.380362 0.0488886 35 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 44 2000 10 nonlinear nonlinear 0.5945 0.4389732 0.4223314 0.0166419 0.5288688 0.4742740 1.364599 0.8157 0.5290 0.4855356 1.379262 0.0632042 40 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 45 2000 10 nonlinear nonlinear 0.6295 0.4296314 0.4204089 0.0092224 0.5267576 0.4737288 1.354343 0.8751 0.5997 0.4758992 1.380039 0.0554902 44 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 46 2000 10 nonlinear nonlinear 0.5725 0.4269549 0.4196267 0.0073282 0.5277385 0.4722114 1.350914 0.8901 0.5773 0.4771933 1.380099 0.0575667 38 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 47 2000 10 nonlinear nonlinear 0.6150 0.4484012 0.4211974 0.0272038 0.5249757 0.4761505 1.373191 0.7497 0.5718 0.4800174 1.393206 0.0588200 39 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 48 2000 10 nonlinear nonlinear 0.6340 0.4314478 0.4218319 0.0096160 0.5281469 0.4748010 1.357830 0.8682 0.5752 0.4800844 1.385836 0.0582526 36 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 49 2000 10 nonlinear nonlinear 0.6040 0.4438583 0.4197365 0.0241218 0.5265055 0.4727114 1.370224 0.7754 0.5804 0.4775863 1.382206 0.0578498 39 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 50 2000 10 nonlinear nonlinear 0.6400 0.4359822 0.4200255 0.0159567 0.5269990 0.4728421 1.373797 0.8239 0.5976 0.4754277 1.387130 0.0554021 37 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 51 2000 10 nonlinear nonlinear 0.6105 0.4357673 0.4222243 0.0135431 0.5271669 0.4751877 1.367129 0.8368 0.5662 0.4810702 1.384466 0.0588459 35 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 52 2000 10 nonlinear nonlinear 0.5945 0.4505933 0.4216997 0.0288937 0.5264098 0.4747119 1.365830 0.7519 0.5778 0.4812554 1.387652 0.0595557 46 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 53 2000 10 nonlinear nonlinear 0.6340 0.4350103 0.4195021 0.0155082 0.5251547 0.4730189 1.367204 0.8326 0.6292 0.4700336 1.388978 0.0505315 39 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 54 2000 10 nonlinear nonlinear 0.6010 0.4419344 0.4213386 0.0205958 0.5283144 0.4733115 1.360161 0.7965 0.6021 0.4758489 1.377559 0.0545103 36 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 55 2000 10 nonlinear nonlinear 0.6195 0.4256827 0.4206398 0.0050429 0.5261080 0.4748450 1.348005 0.9083 0.5709 0.4792639 1.378982 0.0586241 42 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 56 2000 10 nonlinear nonlinear 0.6525 0.4301807 0.4203133 0.0098674 0.5259114 0.4740935 1.362967 0.8718 0.5872 0.4779931 1.385737 0.0576798 40 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 57 2000 10 nonlinear nonlinear 0.6015 0.4288262 0.4209451 0.0078811 0.5283759 0.4732364 1.358005 0.8827 0.5699 0.4791473 1.388453 0.0582022 45 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 58 2000 10 nonlinear nonlinear 0.5790 0.4309909 0.4182000 0.0127910 0.5244953 0.4715003 1.350898 0.8438 0.5767 0.4761773 1.373867 0.0579774 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 59 2000 10 nonlinear nonlinear 0.5725 0.4450152 0.4204159 0.0245993 0.5241732 0.4743000 1.365635 0.7804 0.6037 0.4753063 1.383496 0.0548904 41 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 60 2000 10 nonlinear nonlinear 0.5515 0.4373689 0.4216354 0.0157335 0.5255192 0.4755631 1.365490 0.8232 0.5938 0.4770099 1.392259 0.0553745 38 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 61 2000 10 nonlinear nonlinear 0.5970 0.4287278 0.4198696 0.0088582 0.5265317 0.4728102 1.357088 0.8783 0.5966 0.4755286 1.379622 0.0556590 42 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 62 2000 10 nonlinear nonlinear 0.5970 0.4283218 0.4206272 0.0076946 0.5261641 0.4740206 1.360936 0.8818 0.5614 0.4800701 1.382793 0.0594429 39 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 63 2000 10 nonlinear nonlinear 0.6370 0.4509815 0.4219534 0.0290281 0.5275960 0.4758692 1.370929 0.7534 0.5836 0.4789953 1.386839 0.0570419 40 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 64 2000 10 nonlinear nonlinear 0.5935 0.4365600 0.4209375 0.0156226 0.5258583 0.4741211 1.362312 0.8214 0.6167 0.4724600 1.381989 0.0515226 44 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 65 2000 10 nonlinear nonlinear 0.6260 0.4433698 0.4205446 0.0228253 0.5256620 0.4739905 1.371776 0.7776 0.5833 0.4776898 1.390398 0.0571453 38 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 66 2000 10 nonlinear nonlinear 0.6275 0.4331010 0.4218958 0.0112052 0.5276664 0.4752701 1.355395 0.8569 0.5639 0.4814223 1.383311 0.0595265 39 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 67 2000 10 nonlinear nonlinear 0.5860 0.4383297 0.4211800 0.0171497 0.5266219 0.4746247 1.366011 0.8141 0.5708 0.4781227 1.385166 0.0569427 43 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 68 2000 10 nonlinear nonlinear 0.6090 0.4362190 0.4205074 0.0157116 0.5269338 0.4738000 1.356418 0.8274 0.5567 0.4815019 1.376383 0.0609945 41 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 69 2000 10 nonlinear nonlinear 0.5960 0.4304691 0.4218521 0.0086170 0.5271233 0.4741026 1.359477 0.8731 0.5628 0.4811975 1.378300 0.0593455 41 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 70 2000 10 nonlinear nonlinear 0.6490 0.4332660 0.4193983 0.0138677 0.5257612 0.4724780 1.360052 0.8414 0.6077 0.4739398 1.382658 0.0545415 43 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 71 2000 10 nonlinear nonlinear 0.5800 0.4712202 0.4209831 0.0502371 0.5275639 0.4737105 1.373385 0.5964 0.5590 0.4788316 1.384548 0.0578484 39 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 72 2000 10 nonlinear nonlinear 0.6070 0.4399513 0.4217929 0.0181585 0.5263565 0.4749819 1.363916 0.8120 0.5853 0.4800769 1.382712 0.0582841 45 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 73 2000 10 nonlinear nonlinear 0.5935 0.4321134 0.4208464 0.0112670 0.5265301 0.4739016 1.372111 0.8530 0.5793 0.4779025 1.389409 0.0570561 39 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 74 2000 10 nonlinear nonlinear 0.6045 0.4306694 0.4212448 0.0094246 0.5267579 0.4749108 1.352907 0.8721 0.6059 0.4763096 1.380420 0.0550648 33 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 75 2000 10 nonlinear nonlinear 0.5505 0.4317482 0.4205634 0.0111848 0.5266090 0.4732319 1.363843 0.8580 0.5225 0.4868698 1.382100 0.0663064 38 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 76 2000 10 nonlinear nonlinear 0.5860 0.4382983 0.4195889 0.0187094 0.5261696 0.4729861 1.363260 0.8031 0.5562 0.4811765 1.385385 0.0615876 48 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 77 2000 10 nonlinear nonlinear 0.6325 0.4306657 0.4210179 0.0096477 0.5261914 0.4742632 1.359836 0.8666 0.5953 0.4767905 1.384979 0.0557725 38 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 78 2000 10 nonlinear nonlinear 0.6130 0.4324348 0.4204666 0.0119682 0.5252059 0.4747574 1.357854 0.8507 0.5500 0.4813758 1.383911 0.0609092 45 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 79 2000 10 nonlinear nonlinear 0.5880 0.4344297 0.4215258 0.0129039 0.5265548 0.4742880 1.372059 0.8468 0.5797 0.4785601 1.387321 0.0570343 38 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 80 2000 10 nonlinear nonlinear 0.5860 0.4473216 0.4205328 0.0267888 0.5261105 0.4733685 1.363176 0.7447 0.5652 0.4764266 1.385129 0.0558938 36 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 81 2000 10 nonlinear nonlinear 0.6385 0.4658143 0.4206155 0.0451988 0.5261577 0.4748947 1.357845 0.6275 0.5601 0.4803373 1.381021 0.0597219 39 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 82 2000 10 nonlinear nonlinear 0.6390 0.4246785 0.4210728 0.0036057 0.5272038 0.4737021 1.356151 0.9177 0.5612 0.4803212 1.378037 0.0592485 32 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 83 2000 10 nonlinear nonlinear 0.6055 0.4342624 0.4203612 0.0139012 0.5249887 0.4743267 1.362592 0.8337 0.5302 0.4836860 1.382190 0.0633248 36 0.7 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 84 2000 10 nonlinear nonlinear 0.6285 0.4369739 0.4202106 0.0167633 0.5276390 0.4718516 1.362567 0.8197 0.6214 0.4724779 1.379684 0.0522673 46 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 85 2000 10 nonlinear nonlinear 0.6335 0.4409949 0.4197728 0.0212221 0.5271098 0.4729325 1.363158 0.7969 0.5648 0.4783964 1.378661 0.0586236 46 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 86 2000 10 nonlinear nonlinear 0.5650 0.4324022 0.4207117 0.0116905 0.5271406 0.4732370 1.358877 0.8497 0.5446 0.4838090 1.386800 0.0630972 42 0.5 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 87 2000 10 nonlinear nonlinear 0.5935 0.4280526 0.4206483 0.0074043 0.5260898 0.4743420 1.358837 0.8879 0.5532 0.4817408 1.384841 0.0610925 40 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 88 2000 10 nonlinear nonlinear 0.6140 0.4259445 0.4191294 0.0068151 0.5241629 0.4727198 1.361062 0.8864 0.6019 0.4732020 1.384599 0.0540726 36 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 89 2000 10 nonlinear nonlinear 0.5870 0.4250534 0.4216652 0.0033882 0.5273666 0.4758038 1.354982 0.9280 0.5973 0.4778629 1.382589 0.0561978 41 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 90 2000 10 nonlinear nonlinear 0.6155 0.4340963 0.4202521 0.0138442 0.5247841 0.4745009 1.365720 0.8420 0.5852 0.4767944 1.380294 0.0565422 47 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 91 2000 10 nonlinear nonlinear 0.6015 0.4272104 0.4204713 0.0067391 0.5255952 0.4743351 1.357073 0.8896 0.5929 0.4770162 1.387573 0.0565450 39 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 92 2000 10 nonlinear nonlinear 0.6320 0.4427792 0.4204450 0.0223342 0.5252614 0.4740074 1.372711 0.7818 0.5790 0.4771340 1.392199 0.0566890 40 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 93 2000 10 nonlinear nonlinear 0.5680 0.4300903 0.4202114 0.0098788 0.5275618 0.4735423 1.357961 0.8710 0.5982 0.4752809 1.382432 0.0550694 34 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 94 2000 10 nonlinear nonlinear 0.6150 0.4266050 0.4209071 0.0056979 0.5258108 0.4735610 1.349413 0.8993 0.5908 0.4765940 1.376040 0.0556869 35 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 95 2000 10 nonlinear nonlinear 0.5785 0.4354270 0.4210662 0.0143609 0.5263938 0.4738374 1.357967 0.8290 0.5463 0.4827449 1.377047 0.0616787 40 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 96 2000 10 nonlinear nonlinear 0.6145 0.4330253 0.4205542 0.0124711 0.5247335 0.4743752 1.355307 0.8469 0.6091 0.4735984 1.379776 0.0530442 34 0.9 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 97 2000 10 nonlinear nonlinear 0.6040 0.4302377 0.4189951 0.0112426 0.5239544 0.4732694 1.363087 0.8593 0.5861 0.4763870 1.382993 0.0573919 36 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 98 2000 10 nonlinear nonlinear 0.5790 0.4306738 0.4204165 0.0102573 0.5265579 0.4740519 1.364224 0.8649 0.5525 0.4810449 1.386749 0.0606284 37 0.8 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 99 2000 10 nonlinear nonlinear 0.5990 0.4529193 0.4223740 0.0305453 0.5263849 0.4757470 1.373579 0.7330 0.5725 0.4801681 1.384520 0.0577941 41 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
24 100 2000 10 nonlinear nonlinear 0.5620 0.4598912 0.4203059 0.0395853 0.5265578 0.4736951 1.375801 0.6847 0.5299 0.4848170 1.379246 0.0645112 40 1.0 1.0 1.0 1.0000000 1.0000000 1 1 1 1 1 1 1
  1. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear

Summary statistics

  • bsim.deviance: deviance from Bayesian single index model (BSIM)

  • sng.bayes.deviance: deviance from Bayesian linear model (BLM)

  • bsim.accuracy：the proportion of correct decision (PCD) from BSIM

  • sng.bayes.accuracy: PCD from BLM

  • bsim.value: the expected outcome under the treatment regime using BSIM

  • sng.bayes.value: the expected outcome under the treatment regime using BLM

  • opt.value: the expected outcome under the treatment regime using true value of parameters in for the data generation

#myVars <- c("bsim.deviance","bsim.accuracy","sng.bayes.deviance","sng.bayes.accuracy",
#"bsim.value","sng.bayes.value","opt.value")
#paste(" ",paste(myVars, collapse=" + "), sep=" ~ ")

table1(~ bsim.deviance +  sng.bayes.deviance + 
         bsim.accuracy + sng.bayes.accuracy + bsim.value + 
         sng.bayes.value + opt.value| scenario,data=results,
       overall=FALSE,transpose=TRUE,topclass="Rtable1-zebra")
bsim.deviance sng.bayes.deviance bsim.accuracy sng.bayes.accuracy bsim.value sng.bayes.value opt.value
1. n= 500, p= 5, g.choice=linear, m.choice=linear
(N=100)		 :
Mean (SD): 1.34 (0.0129)
Median [Min, Max]: 1.34 [1.32, 1.39]		 :
Mean (SD): 1.34 (0.0125)
Median [Min, Max]: 1.34 [1.31, 1.37]		 :
Mean (SD): 0.720 (0.110)
Median [Min, Max]: 0.735 [0.493, 0.922]		 :
Mean (SD): 0.826 (0.0615)
Median [Min, Max]: 0.823 [0.671, 0.936]		 :
Mean (SD): 0.463 (0.0166)
Median [Min, Max]: 0.459 [0.439, 0.503]		 :
Mean (SD): 0.447 (0.00665)
Median [Min, Max]: 0.446 [0.437, 0.468]		 :
Mean (SD): 0.436 (0.00114)
Median [Min, Max]: 0.436 [0.434, 0.439]
2. n=1000, p= 5, g.choice=linear, m.choice=linear
(N=100)		 :
Mean (SD): 1.33 (0.00729)
Median [Min, Max]: 1.33 [1.31, 1.35]		 :
Mean (SD): 1.32 (0.00658)
Median [Min, Max]: 1.32 [1.31, 1.34]		 :
Mean (SD): 0.779 (0.0863)
Median [Min, Max]: 0.781 [0.580, 0.971]		 :
Mean (SD): 0.866 (0.0431)
Median [Min, Max]: 0.870 [0.739, 0.954]		 :
Mean (SD): 0.453 (0.0106)
Median [Min, Max]: 0.452 [0.436, 0.480]		 :
Mean (SD): 0.443 (0.00413)
Median [Min, Max]: 0.442 [0.435, 0.459]		 :
Mean (SD): 0.437 (0.00103)
Median [Min, Max]: 0.437 [0.434, 0.440]
3. n=2000, p= 5, g.choice=linear, m.choice=linear
(N=100)		 :
Mean (SD): 1.32 (0.00533)
Median [Min, Max]: 1.32 [1.30, 1.33]		 :
Mean (SD): 1.32 (0.00572)
Median [Min, Max]: 1.32 [1.30, 1.33]		 :
Mean (SD): 0.848 (0.0650)
Median [Min, Max]: 0.862 [0.669, 0.959]		 :
Mean (SD): 0.905 (0.0315)
Median [Min, Max]: 0.908 [0.805, 0.970]		 :
Mean (SD): 0.444 (0.00635)
Median [Min, Max]: 0.443 [0.436, 0.463]		 :
Mean (SD): 0.440 (0.00240)
Median [Min, Max]: 0.439 [0.435, 0.447]		 :
Mean (SD): 0.437 (0.00109)
Median [Min, Max]: 0.437 [0.434, 0.439]
4. n= 500, p=10, g.choice=linear, m.choice=linear
(N=100)		 :
Mean (SD): 1.36 (0.0153)
Median [Min, Max]: 1.35 [1.33, 1.40]		 :
Mean (SD): 1.36 (0.0171)
Median [Min, Max]: 1.36 [1.33, 1.43]		 :
Mean (SD): 0.671 (0.101)
Median [Min, Max]: 0.680 [0.367, 0.872]		 :
Mean (SD): 0.771 (0.0517)
Median [Min, Max]: 0.774 [0.632, 0.906]		 :
Mean (SD): 0.469 (0.0167)
Median [Min, Max]: 0.466 [0.443, 0.525]		 :
Mean (SD): 0.453 (0.00704)
Median [Min, Max]: 0.452 [0.439, 0.471]		 :
Mean (SD): 0.436 (0.00108)
Median [Min, Max]: 0.436 [0.434, 0.438]
5. n=1000, p=10, g.choice=linear, m.choice=linear
(N=100)		 :
Mean (SD): 1.34 (0.00907)
Median [Min, Max]: 1.34 [1.32, 1.36]		 :
Mean (SD): 1.33 (0.00997)
Median [Min, Max]: 1.33 [1.31, 1.36]		 :
Mean (SD): 0.757 (0.0755)
Median [Min, Max]: 0.763 [0.457, 0.885]		 :
Mean (SD): 0.820 (0.0383)
Median [Min, Max]: 0.820 [0.718, 0.901]		 :
Mean (SD): 0.455 (0.0107)
Median [Min, Max]: 0.453 [0.439, 0.505]		 :
Mean (SD): 0.446 (0.00446)
Median [Min, Max]: 0.446 [0.438, 0.461]		 :
Mean (SD): 0.436 (0.00133)
Median [Min, Max]: 0.436 [0.433, 0.439]
6. n=2000, p=10, g.choice=linear, m.choice=linear
(N=100)		 :
Mean (SD): 1.32 (0.00564)
Median [Min, Max]: 1.32 [1.31, 1.34]		 :
Mean (SD): 1.32 (0.00595)
Median [Min, Max]: 1.32 [1.31, 1.34]		 :
Mean (SD): 0.811 (0.0680)
Median [Min, Max]: 0.830 [0.654, 0.912]		 :
Mean (SD): 0.867 (0.0340)
Median [Min, Max]: 0.866 [0.769, 0.932]		 :
Mean (SD): 0.448 (0.00767)
Median [Min, Max]: 0.446 [0.438, 0.469]		 :
Mean (SD): 0.442 (0.00306)
Median [Min, Max]: 0.442 [0.436, 0.453]		 :
Mean (SD): 0.436 (0.00109)
Median [Min, Max]: 0.436 [0.433, 0.439]
7. n= 500, p= 5, g.choice=nonlinear, m.choice=linear
(N=100)		 :
Mean (SD): 1.34 (0.0163)
Median [Min, Max]: 1.34 [1.32, 1.40]		 :
Mean (SD): 1.35 (0.0108)
Median [Min, Max]: 1.35 [1.33, 1.39]		 :
Mean (SD): 0.699 (0.102)
Median [Min, Max]: 0.708 [0.492, 0.904]		 :
Mean (SD): 0.589 (0.0510)
Median [Min, Max]: 0.582 [0.430, 0.757]		 :
Mean (SD): 0.461 (0.0181)
Median [Min, Max]: 0.459 [0.429, 0.500]		 :
Mean (SD): 0.480 (0.0102)
Median [Min, Max]: 0.479 [0.453, 0.524]		 :
Mean (SD): 0.423 (0.00124)
Median [Min, Max]: 0.423 [0.420, 0.426]
8. n=1000, p= 5, g.choice=nonlinear, m.choice=linear
(N=100)		 :
Mean (SD): 1.32 (0.00860)
Median [Min, Max]: 1.32 [1.31, 1.35]		 :
Mean (SD): 1.34 (0.00627)
Median [Min, Max]: 1.34 [1.33, 1.36]		 :
Mean (SD): 0.791 (0.0941)
Median [Min, Max]: 0.817 [0.423, 0.930]		 :
Mean (SD): 0.594 (0.0485)
Median [Min, Max]: 0.580 [0.510, 0.727]		 :
Mean (SD): 0.445 (0.0153)
Median [Min, Max]: 0.441 [0.425, 0.494]		 :
Mean (SD): 0.477 (0.00763)
Median [Min, Max]: 0.480 [0.458, 0.491]		 :
Mean (SD): 0.423 (0.00111)
Median [Min, Max]: 0.423 [0.420, 0.426]
9. n=2000, p= 5, g.choice=nonlinear, m.choice=linear
(N=100)		 :
Mean (SD): 1.32 (0.00638)
Median [Min, Max]: 1.32 [1.30, 1.34]		 :
Mean (SD): 1.34 (0.00497)
Median [Min, Max]: 1.33 [1.32, 1.35]		 :
Mean (SD): 0.857 (0.0601)
Median [Min, Max]: 0.868 [0.624, 0.966]		 :
Mean (SD): 0.582 (0.0537)
Median [Min, Max]: 0.579 [0.472, 0.723]		 :
Mean (SD): 0.435 (0.00860)
Median [Min, Max]: 0.432 [0.423, 0.469]		 :
Mean (SD): 0.479 (0.00801)
Median [Min, Max]: 0.480 [0.459, 0.497]		 :
Mean (SD): 0.423 (0.00121)
Median [Min, Max]: 0.423 [0.420, 0.426]
10. n= 500, p=10, g.choice=nonlinear, m.choice=linear
(N=100)		 :
Mean (SD): 1.36 (0.0182)
Median [Min, Max]: 1.36 [1.33, 1.41]		 :
Mean (SD): 1.38 (0.0186)
Median [Min, Max]: 1.38 [1.35, 1.43]		 :
Mean (SD): 0.632 (0.0898)
Median [Min, Max]: 0.632 [0.434, 0.840]		 :
Mean (SD): 0.574 (0.0339)
Median [Min, Max]: 0.569 [0.496, 0.713]		 :
Mean (SD): 0.472 (0.0170)
Median [Min, Max]: 0.472 [0.434, 0.521]		 :
Mean (SD): 0.482 (0.00652)
Median [Min, Max]: 0.482 [0.460, 0.499]		 :
Mean (SD): 0.423 (0.00120)
Median [Min, Max]: 0.423 [0.420, 0.426]
11. n=1000, p=10, g.choice=nonlinear, m.choice=linear
(N=100)		 :
Mean (SD): 1.34 (0.0106)
Median [Min, Max]: 1.33 [1.31, 1.36]		 :
Mean (SD): 1.35 (0.00967)
Median [Min, Max]: 1.35 [1.33, 1.38]		 :
Mean (SD): 0.716 (0.0946)
Median [Min, Max]: 0.736 [0.489, 0.893]		 :
Mean (SD): 0.577 (0.0256)
Median [Min, Max]: 0.573 [0.525, 0.661]		 :
Mean (SD): 0.457 (0.0165)
Median [Min, Max]: 0.455 [0.428, 0.507]		 :
Mean (SD): 0.481 (0.00476)
Median [Min, Max]: 0.480 [0.469, 0.495]		 :
Mean (SD): 0.423 (0.00120)
Median [Min, Max]: 0.423 [0.420, 0.426]
12. n=2000, p=10, g.choice=nonlinear, m.choice=linear
(N=100)		 :
Mean (SD): 1.32 (0.00714)
Median [Min, Max]: 1.32 [1.31, 1.34]		 :
Mean (SD): 1.34 (0.00616)
Median [Min, Max]: 1.34 [1.33, 1.36]		 :
Mean (SD): 0.807 (0.0617)
Median [Min, Max]: 0.823 [0.566, 0.920]		 :
Mean (SD): 0.592 (0.0276)
Median [Min, Max]: 0.589 [0.525, 0.674]		 :
Mean (SD): 0.441 (0.00959)
Median [Min, Max]: 0.439 [0.427, 0.476]		 :
Mean (SD): 0.477 (0.00420)
Median [Min, Max]: 0.478 [0.463, 0.487]		 :
Mean (SD): 0.423 (0.00126)
Median [Min, Max]: 0.423 [0.420, 0.426]
13. n= 500, p= 5, g.choice=linear, m.choice=nonlinear
(N=100)		 :
Mean (SD): 1.38 (0.0128)
Median [Min, Max]: 1.38 [1.36, 1.43]		 :
Mean (SD): 1.38 (0.0129)
Median [Min, Max]: 1.38 [1.35, 1.42]		 :
Mean (SD): 0.744 (0.0947)
Median [Min, Max]: 0.756 [0.489, 0.923]		 :
Mean (SD): 0.824 (0.0547)
Median [Min, Max]: 0.829 [0.672, 0.941]		 :
Mean (SD): 0.457 (0.0138)
Median [Min, Max]: 0.454 [0.436, 0.500]		 :
Mean (SD): 0.445 (0.00654)
Median [Min, Max]: 0.443 [0.434, 0.466]		 :
Mean (SD): 0.434 (0.00105)
Median [Min, Max]: 0.434 [0.432, 0.437]
14. n=1000, p= 5, g.choice=linear, m.choice=nonlinear
(N=100)		 :
Mean (SD): 1.37 (0.00639)
Median [Min, Max]: 1.37 [1.36, 1.39]		 :
Mean (SD): 1.37 (0.00594)
Median [Min, Max]: 1.36 [1.35, 1.38]		 :
Mean (SD): 0.822 (0.0786)
Median [Min, Max]: 0.838 [0.542, 0.959]		 :
Mean (SD): 0.877 (0.0405)
Median [Min, Max]: 0.881 [0.757, 0.980]		 :
Mean (SD): 0.446 (0.00984)
Median [Min, Max]: 0.443 [0.434, 0.495]		 :
Mean (SD): 0.440 (0.00365)
Median [Min, Max]: 0.439 [0.433, 0.454]		 :
Mean (SD): 0.434 (0.00108)
Median [Min, Max]: 0.434 [0.432, 0.438]
15. n=2000, p= 5, g.choice=linear, m.choice=nonlinear
(N=100)		 :
Mean (SD): 1.36 (0.00477)
Median [Min, Max]: 1.36 [1.35, 1.37]		 :
Mean (SD): 1.36 (0.00493)
Median [Min, Max]: 1.36 [1.35, 1.37]		 :
Mean (SD): 0.864 (0.0554)
Median [Min, Max]: 0.874 [0.692, 0.960]		 :
Mean (SD): 0.905 (0.0279)
Median [Min, Max]: 0.909 [0.831, 0.956]		 :
Mean (SD): 0.441 (0.00582)
Median [Min, Max]: 0.440 [0.434, 0.467]		 :
Mean (SD): 0.437 (0.00204)
Median [Min, Max]: 0.437 [0.432, 0.442]		 :
Mean (SD): 0.434 (0.00100)
Median [Min, Max]: 0.434 [0.431, 0.437]
16. n= 500, p=10, g.choice=linear, m.choice=nonlinear
(N=100)		 :
Mean (SD): 1.40 (0.0142)
Median [Min, Max]: 1.40 [1.37, 1.44]		 :
Mean (SD): 1.40 (0.0148)
Median [Min, Max]: 1.40 [1.37, 1.44]		 :
Mean (SD): 0.691 (0.0880)
Median [Min, Max]: 0.704 [0.489, 0.869]		 :
Mean (SD): 0.771 (0.0549)
Median [Min, Max]: 0.771 [0.601, 0.881]		 :
Mean (SD): 0.464 (0.0144)
Median [Min, Max]: 0.461 [0.440, 0.501]		 :
Mean (SD): 0.452 (0.00783)
Median [Min, Max]: 0.451 [0.439, 0.480]		 :
Mean (SD): 0.434 (0.00103)
Median [Min, Max]: 0.434 [0.432, 0.437]
17. n=1000, p=10, g.choice=linear, m.choice=nonlinear
(N=100)		 :
Mean (SD): 1.38 (0.00759)
Median [Min, Max]: 1.38 [1.36, 1.40]		 :
Mean (SD): 1.38 (0.00935)
Median [Min, Max]: 1.38 [1.36, 1.42]		 :
Mean (SD): 0.766 (0.0813)
Median [Min, Max]: 0.777 [0.456, 0.906]		 :
Mean (SD): 0.825 (0.0462)
Median [Min, Max]: 0.826 [0.712, 0.919]		 :
Mean (SD): 0.453 (0.0112)
Median [Min, Max]: 0.451 [0.437, 0.495]		 :
Mean (SD): 0.445 (0.00535)
Median [Min, Max]: 0.443 [0.436, 0.460]		 :
Mean (SD): 0.434 (0.000918)
Median [Min, Max]: 0.434 [0.432, 0.437]
18. n=2000, p=10, g.choice=linear, m.choice=nonlinear
(N=100)		 :
Mean (SD): 1.37 (0.00455)
Median [Min, Max]: 1.37 [1.35, 1.38]		 :
Mean (SD): 1.37 (0.00490)
Median [Min, Max]: 1.37 [1.35, 1.38]		 :
Mean (SD): 0.820 (0.0572)
Median [Min, Max]: 0.834 [0.676, 0.922]		 :
Mean (SD): 0.868 (0.0330)
Median [Min, Max]: 0.870 [0.754, 0.931]		 :
Mean (SD): 0.445 (0.00628)
Median [Min, Max]: 0.443 [0.436, 0.464]		 :
Mean (SD): 0.440 (0.00315)
Median [Min, Max]: 0.440 [0.435, 0.454]		 :
Mean (SD): 0.434 (0.000891)
Median [Min, Max]: 0.434 [0.432, 0.436]
19. n= 500, p= 5, g.choice=nonlinear, m.choice=nonlinear
(N=100)		 :
Mean (SD): 1.38 (0.0149)
Median [Min, Max]: 1.38 [1.35, 1.42]		 :
Mean (SD): 1.40 (0.0117)
Median [Min, Max]: 1.39 [1.38, 1.43]		 :
Mean (SD): 0.714 (0.114)
Median [Min, Max]: 0.734 [0.432, 0.912]		 :
Mean (SD): 0.580 (0.0468)
Median [Min, Max]: 0.576 [0.472, 0.705]		 :
Mean (SD): 0.457 (0.0206)
Median [Min, Max]: 0.452 [0.425, 0.512]		 :
Mean (SD): 0.480 (0.00874)
Median [Min, Max]: 0.479 [0.460, 0.508]		 :
Mean (SD): 0.421 (0.000993)
Median [Min, Max]: 0.421 [0.418, 0.424]
20. n=1000, p= 5, g.choice=nonlinear, m.choice=nonlinear
(N=100)		 :
Mean (SD): 1.37 (0.00869)
Median [Min, Max]: 1.37 [1.35, 1.40]		 :
Mean (SD): 1.39 (0.00589)
Median [Min, Max]: 1.38 [1.37, 1.41]		 :
Mean (SD): 0.791 (0.0914)
Median [Min, Max]: 0.809 [0.508, 0.934]		 :
Mean (SD): 0.584 (0.0484)
Median [Min, Max]: 0.579 [0.476, 0.770]		 :
Mean (SD): 0.443 (0.0156)
Median [Min, Max]: 0.440 [0.423, 0.494]		 :
Mean (SD): 0.478 (0.00739)
Median [Min, Max]: 0.478 [0.451, 0.493]		 :
Mean (SD): 0.421 (0.00108)
Median [Min, Max]: 0.421 [0.419, 0.423]
21. n=2000, p= 5, g.choice=nonlinear, m.choice=nonlinear
(N=100)		 :
Mean (SD): 1.36 (0.00419)
Median [Min, Max]: 1.36 [1.35, 1.37]		 :
Mean (SD): 1.38 (0.00337)
Median [Min, Max]: 1.38 [1.37, 1.39]		 :
Mean (SD): 0.876 (0.0431)
Median [Min, Max]: 0.879 [0.710, 0.957]		 :
Mean (SD): 0.577 (0.0438)
Median [Min, Max]: 0.576 [0.461, 0.689]		 :
Mean (SD): 0.430 (0.00581)
Median [Min, Max]: 0.429 [0.420, 0.453]		 :
Mean (SD): 0.479 (0.00636)
Median [Min, Max]: 0.478 [0.461, 0.494]		 :
Mean (SD): 0.421 (0.00103)
Median [Min, Max]: 0.421 [0.419, 0.423]
22. n= 500, p=10, g.choice=nonlinear, m.choice=nonlinear
(N=100)		 :
Mean (SD): 1.40 (0.0161)
Median [Min, Max]: 1.40 [1.36, 1.45]		 :
Mean (SD): 1.42 (0.0154)
Median [Min, Max]: 1.42 [1.39, 1.47]		 :
Mean (SD): 0.631 (0.0930)
Median [Min, Max]: 0.621 [0.451, 0.881]		 :
Mean (SD): 0.572 (0.0345)
Median [Min, Max]: 0.570 [0.470, 0.665]		 :
Mean (SD): 0.472 (0.0185)
Median [Min, Max]: 0.472 [0.428, 0.522]		 :
Mean (SD): 0.482 (0.00737)
Median [Min, Max]: 0.481 [0.466, 0.505]		 :
Mean (SD): 0.421 (0.000842)
Median [Min, Max]: 0.421 [0.418, 0.423]
23. n=1000, p=10, g.choice=nonlinear, m.choice=nonlinear
(N=100)		 :
Mean (SD): 1.37 (0.00971)
Median [Min, Max]: 1.37 [1.36, 1.41]		 :
Mean (SD): 1.40 (0.00818)
Median [Min, Max]: 1.40 [1.38, 1.42]		 :
Mean (SD): 0.751 (0.0831)
Median [Min, Max]: 0.766 [0.497, 0.911]		 :
Mean (SD): 0.584 (0.0308)
Median [Min, Max]: 0.577 [0.511, 0.701]		 :
Mean (SD): 0.449 (0.0149)
Median [Min, Max]: 0.446 [0.425, 0.500]		 :
Mean (SD): 0.478 (0.00516)
Median [Min, Max]: 0.479 [0.462, 0.495]		 :
Mean (SD): 0.421 (0.000948)
Median [Min, Max]: 0.421 [0.418, 0.423]
24. n=2000, p=10, g.choice=nonlinear, m.choice=nonlinear
(N=100)		 :
Mean (SD): 1.36 (0.00609)
Median [Min, Max]: 1.36 [1.35, 1.38]		 :
Mean (SD): 1.38 (0.00427)
Median [Min, Max]: 1.38 [1.37, 1.40]		 :
Mean (SD): 0.832 (0.0573)
Median [Min, Max]: 0.839 [0.596, 0.929]		 :
Mean (SD): 0.582 (0.0258)
Median [Min, Max]: 0.581 [0.523, 0.666]		 :
Mean (SD): 0.436 (0.00882)
Median [Min, Max]: 0.434 [0.423, 0.471]		 :
Mean (SD): 0.478 (0.00355)
Median [Min, Max]: 0.478 [0.466, 0.487]		 :
Mean (SD): 0.421 (0.000984)
Median [Min, Max]: 0.421 [0.418, 0.423]

3 Deviance

m_dev <- results %>% 
  group_by(replicate) %>% 
  dplyr::summarise(mean_dev_bsim=mean(bsim.deviance), mean_dev_sng=mean(sng.bayes.deviance),
                   sd_dev_bsim=sd(bsim.deviance), sd_dev_sng=sd(sng.bayes.deviance)) 

l_dev <- reshape2::melt(m_dev,id=c("replicate"))
sd <- l_dev %>% subset(variable %in% c("sd_dev_sng","sd_dev_bsim"))
colnames(sd) <- c("replicate","variable_sd","value_sd")
mean_dev <- l_dev %>% subset(variable %in% c("mean_dev_bsim","mean_dev_sng"))
plot_dev <- cbind(mean_dev,sd[,-1])

plot_dev$replicate <- as.factor(plot_dev$replicate)

ggplot(plot_dev, aes(x=replicate, y=value, color=variable)) + 
  geom_point()+
  geom_errorbar(aes(ymin=value - value_sd, ymax=value + value_sd), width=.2,
                position=position_dodge())+ 
  labs(title="Mean (+- standard deviation) of deviance based on 100 simulations",
        y = "Deviance", x= "Scenario")+ theme(legend.position="bottom")+
  scale_color_discrete(name = "Model", labels = c("BSIM", "BLM"))

dev <- results[,c("replicate","bsim.deviance","sng.bayes.deviance")]

dev <- reshape2::melt(dev,id=c("replicate"))
dev$replicate <- as.factor(dev$replicate)
ggplot(dev, aes(x=replicate, y=value,fill=variable)) +
  geom_boxplot(outlier.size=0.5)+ 
  labs(title="Boxplot of deviance based on 100 simulations",
        y = "Deviance", x= "Scenario")+ theme(legend.position="bottom")+
  scale_fill_discrete(name = "Model", labels = c("BSIM", "BLM"))

4 The expected outcome under the treatment regime

Different from the stan model(in iImulation_BSIMML_stan.html), the optimal treatment decision rule depends on the contrast:\(\Delta = \beta_0+\boldsymbol{X_i}^\top\boldsymbol{\beta}\).

m_e_y <- results %>% 
  group_by(replicate) %>% 
  dplyr::summarise(mean_y_bsim=mean(bsim.value), mean_y_sng=mean(sng.bayes.value),
                   mean_y_optim = mean(opt.value),
                   sd_y_bsim=sd(bsim.value), sd_y_sng=sd(sng.bayes.value),
                  sd_y_optim = sd(opt.value), ) 

l_y <- reshape2::melt(m_e_y,id=c("replicate"))
sd <- l_y %>% subset(variable %in% c("sd_y_sng","sd_y_bsim","sd_y_optim"))
colnames(sd) <- c("replicate","variable_sd","value_sd")
mean_y <- l_y %>% subset(variable %in% c("mean_y_bsim","mean_y_sng","mean_y_optim"))
plot_y <- cbind(mean_y,sd[,-1])

plot_y$replicate <- as.factor(plot_y$replicate)

ggplot(plot_y, aes(x=replicate, y=value, color=variable)) + 
  geom_point()+
  geom_errorbar(aes(ymin=value - value_sd, ymax=value + value_sd), width=.2,
                position=position_dodge())+ 
  labs(title="Mean (+- SD) of the expected outcome under the treatment regime",
        y = "The expected outcome under the treatment regime", x= "Scenario")+ theme(legend.position="bottom")+
  scale_color_discrete(name = "Model", labels = c("BSIM", "BLM","True optimal value using true value of parameters"))

e_y <- results[,c("replicate","bsim.value","sng.bayes.value","opt.value")]

e_y <- reshape2::melt(e_y,id=c("replicate"))
e_y$replicate <- as.factor(e_y$replicate)
ggplot(e_y, aes(x=replicate, y=value,fill=variable)) +
  geom_boxplot(outlier.size=0.5)+ 
  labs(title="Boxplot of the expected outcome under the treatment regime",
        y = "The expected outcome under the treatment regime", x= "Scenario")+ theme(legend.position="bottom")+
  scale_fill_discrete(name = "Model", labels = c("BSIM", "BLM","True optimal value using true value of parameters"))

5 PCD

m_pcd <- results %>% 
  group_by(replicate) %>% 
  dplyr::summarise(mean_pcd_bsim=mean(bsim.accuracy), mean_pcd_sng=mean(sng.bayes.accuracy),
                   sd_pcd_bsim=sd(bsim.accuracy), sd_pcd_sng=sd(sng.bayes.accuracy)) 

l_pcd <- reshape2::melt(m_pcd,id=c("replicate"))
sd <- l_pcd %>% subset(variable %in% c("sd_pcd_sng","sd_pcd_bsim"))
colnames(sd) <- c("replicate","variable_sd","value_sd")
mean_pcd <- l_pcd %>% subset(variable %in% c("mean_pcd_bsim","mean_pcd_sng"))
plot_pcd <- cbind(mean_pcd,sd[,-1])

plot_pcd$replicate <- as.factor(plot_pcd$replicate)

ggplot(plot_pcd, aes(x=replicate, y=value, color=variable)) + 
  geom_point()+
  geom_errorbar(aes(ymin=value - value_sd, ymax=value + value_sd), width=.2,
                position=position_dodge())+ 
  labs(title="Mean (+- SD) of PCD based on 100 simulations",
        y = "PCD", x= "Scenario")+ theme(legend.position="bottom")+
  scale_color_discrete(name = "Model", labels = c("BSIM", "BLM"))

pcd <- results[,c("replicate","bsim.accuracy","sng.bayes.accuracy")]

pcd <- reshape2::melt(pcd,id=c("replicate"))
pcd$replicate <- as.factor(pcd$replicate)
ggplot(pcd, aes(x=replicate, y=value,fill=variable)) +
  geom_boxplot(outlier.size=0.5)+ 
  labs(title="Boxplot of PCD based on 100 simulations",
        y = "PCD", x= "Scenario")+ theme(legend.position="bottom")+
  scale_fill_discrete(name = "Model", labels = c("BSIM", "BLM"))